DE102019206844A1 - Analysis methods and devices for this - Google Patents

Abstract

In order to provide a method for predicting process deviations in a process engineering plant, for example in a paint shop, by means of which process deviations can be easily and reliably predicted, it is proposed according to the invention that the method comprises: automatic creation of a prediction model; prediction of process deviations during operation the process plant using the predictive model.

Description

The present invention relates to a method for error analysis in a process engineering plant, for example in a painting plant.

The present invention is based on the object of providing a method for error analysis in a process engineering system, for example in a painting system, by means of which error situations can be analyzed easily and reliably.

This object is achieved by a method for error analysis in a process engineering system, for example in a painting system.

• - insbesondere automatisches Erkennen einer Fehlersituation in der verfahrenstechnischen Anlage;
• - Speichern eines Fehlersituationsdatensatzes zu der jeweiligen erkannten Fehlersituation in einer Fehlerdatenbank;
• - automatische Ermittlung einer Fehlerursache für die Fehlersituation und/oder automatische Ermittlung von für die Fehlersituation relevanten Prozesswerten auf Basis des Fehlerdatensatzes einer jeweiligen erkannten Fehlersituation.

The method for error analysis in a process plant, for example in a paint shop, preferably includes the following:

• - in particular automatic recognition of an error situation in the process engineering system;
• - Storage of an error situation data record for the respective recognized error situation in a defect database;
• - automatic determination of a cause of the error for the error situation and / or automatic determination of process values relevant to the error situation on the basis of the error data record of a respective recognized error situation.

In the context of this description and the appended claims, process values which are the cause of the error situation are understood to mean, in particular, process values which are the cause of the error situation and / or are associated therewith.

It can be beneficial if the error situation is automatically recognized by means of a reporting system.

• - einer Vorverknüpfung aus einem Meldesystem;
• - einer Zuordnung eines Prozesswerts zu demselben Anlagenteil der verfahrenstechnischen Anlage, in dem die Fehlersituation aufgetreten ist;
• - einer Verknüpfung eines Prozesswerts mit einer historischen Fehlersituation aufgrund einer aktiven Auswahl eines Benutzers;
• - einer aktiven Auswahl des Prozesswerts durch einen Benutzer.

In one embodiment of the method for error analysis in a process plant, one or more process values are automatically linked to the error situation based on one or more of the following linking criteria for the automatic determination of the cause of the error for the error situation and / or the process values relevant to the error situation :

• - a pre-link from a reporting system;
• - an assignment of a process value to the same part of the process plant in which the error situation occurred;
• a link between a process value and a historical error situation based on an active selection by a user;
• - an active selection of the process value by a user.

• - einer Prozessrelevanz der Prozesswerte;
• - einer Position eines Prozesswerts oder eines den Prozesswert ermittelnden Sensors innerhalb der verfahrenstechnischen Anlage;
• - einem Betrag einer Abweichung eines Prozesswerts von einem definierten Prozessfenster und/oder von einem Normalzustand;
• - einer Priorisierung von historischen Prozesswerten in historischen Fehlersituationen;
• - durch Übernehmen einer Priorisierung der Fehlerursache und/oder der Prozesswerte aus einem Meldesystem;
• - einer Priorisierung durch einen Benutzer.

In one embodiment of the method for error analysis in a process engineering system, it is provided that, for the automatic determination of the cause of the error for the error situation and / or the process values relevant to the error situation, an automatic prioritization of the process values linked to the error situation based on one or more of the following prioritization criteria is carried out:

• - a process relevance of the process values;
• a position of a process value or of a sensor determining the process value within the process engineering system;
• an amount of a deviation of a process value from a defined process window and / or from a normal state;
• - a prioritization of historical process values in historical error situations;
• - by adopting a prioritization of the cause of the error and / or the process values from a message system;
• - a prioritization by a user.

A prioritization carried out based on the process relevance of the process values is preferably carried out in such a way that process-critical process values are given higher priority.

In the context of this description and the appended claims, a process-critical process value is understood to mean in particular a process value that is stored in the reporting system as process-critical and / or has been defined as process-critical by a user.

A prioritization carried out based on a position of the process value or a sensor ascertaining the process value within the process engineering system is preferably carried out in such a way that process values that are assigned to the same, a nearby and / or a comparable part of the system are prioritized higher.

In the context of this description and the appended claims, comparable system parts are understood to mean in particular system parts with a similar or identical structure.

Comparable system parts are, for example, identical or similar industrial air supply systems, identical or similar Conditioning modules of an industrial air supply system or pumps or motors of identical or similar construction.

A position of the sensor determining the process value is preferably identified in the process engineering system using a system from a numbering system (so-called “plant numbering system”).

Process values are preferably clearly identified by means of the numbering system.

Process values are preferably prioritized depending on their designation in the numbering system.

For the unambiguous designation of sensors and / or process values, the numbering system preferably includes the designation of a functional unit, the designation of a functional group of the respective functional unit and / or the designation of a functional element of the respective functional group to which the respective sensor and / or process value is assigned.

It can also be favorable if the unique designation of a process value by means of the numbering system includes a designation of a type of the measured variable, for example a temperature, a flow rate, a pressure.

For example, a supply air system of a painting system is a functional unit, a conditioning module of the supply air system being a functional group and a pump of the supply air system being a functional element.

A normal state of a process value is preferably determined by means of a method for anomaly and / or error detection.

A prioritization carried out based on a prioritization of historical process values in historical error situations is preferably carried out in such a way that process values are prioritized analogously to the historical error situation.

• - einer Prozessrelevanz der Prozesswerte;
• - einer Position eines Prozesswerts oder eines den Prozesswert ermittelnden Sensors innerhalb der verfahrenstechnischen Anlage;
• - einem Betrag einer Abweichung eines Prozesswerts von einem definierten Prozessfenster und/oder von einem Normalzustand;
• - einer Priorisierung von historischen Prozesswerten in historischen Fehlersituationen;
• - physikalischen Abhängigkeiten der Prozesswerte.

In one embodiment of the method for error analysis in a process engineering system, further error causes and / or process values are proposed to automatically determine the cause of the error for the error situation and / or the process values relevant to the error situation, the suggestion being based on one or more of the following Proposal criteria is carried out automatically:

• - a process relevance of the process values;
• a position of a process value or of a sensor determining the process value within the process engineering system;
• an amount of a deviation of a process value from a defined process window and / or from a normal state;
• - a prioritization of historical process values in historical error situations;
• - physical dependencies of the process values.

A process-critical process value is preferably suggested.

A proposal made based on a position of the process value or a sensor determining the process value within the process engineering system is preferably made such that process values that are assigned to the same, a nearby and / or a comparable part of the system are more likely to be proposed.

Process values are preferably proposed in the numbering system depending on their designation.

A proposal carried out based on a prioritization of historical process values in historical error situations is preferably made in such a way that process values with a high priority are preferably proposed in the historical error situation.

To determine a proposal based on physical dependencies of the process values, the physical dependencies are preferably defined by a user as an expert rule.

A prioritization of the suggested causes of errors and / or process values can preferably be changed by a user.

• - einer Fehlerklassifikation der historischen Fehlersituation;
• - einer historischen Fehlersituation an demselben oder einem vergleichbaren Anlagenteil;
• - identischen oder ähnlichen Prozesswerten der historischen Fehlersituation zu Prozesswerten der erkannten Fehlersituation.

In one embodiment of the method for error analysis in a process plant, it is provided that historical error situations are determined from an error database using one or more of the following similarity criteria:

• - an error classification of the historical error situation;
• - a historical error situation on the same or a comparable part of the system;
• - identical or similar process values of the historical error situation to process values of the recognized error situation.

Historical error situations with an error classification that is identical to the detected error situation are preferably determined.

An identity or similarity of the process values of the historical error situation to the process values of the recognized error situation is preferably determined by a comparison algorithm.

In an embodiment of the method for error analysis in a process engineering system, it is provided that historical process values are determined from a process database which are identical or similar to process values of the detected error situation.

A process database is preferably searched to determine the historical process values. It can be beneficial if an identity or similarity of the process values is determined by means of a comparison algorithm.

The historical process values are preferably determined automatically.

In one embodiment of the method for error analysis in a process engineering system, it is provided that the historical process values determined are identified as belonging to a historical error situation.

In one embodiment of the method for error analysis in a process engineering system, provision is made for an error situation data set to be stored in a error database for a recognized error situation.

• - eine Fehlerklassifikation der Fehlersituation;
• - mit der Fehlersituation verknüpfte Prozesswerte basierend auf einer Vorverknüpfung aus einem Meldesystem;
• - Informationen über einen Zeitpunkt des Auftretens einer jeweiligen Fehlersituation;
• - Informationen über eine Zeitdauer des Auftretens einer jeweiligen Fehlersituation;
• - Informationen über den Ort des Auftretens einer jeweiligen Fehlersituation;
• - Alarme;
• - Statusmeldungen.

In one embodiment of the method for error analysis in a process plant, it is provided that a respective error identification data record includes one or more of the following error situation data:

• - an error classification of the error situation;
• - Process values linked to the error situation based on a pre-link from a reporting system;
• - Information about a point in time when a respective error situation occurred;
• - Information about the duration of the occurrence of a respective error situation;
• - Information about the location of the occurrence of a respective error situation;
• - alarms;
• - Status messages.

In one embodiment of the method for error analysis in a process plant, it is provided that the error situation data record of a respective error situation includes error identification data for the unambiguous identification of the detected error situation.

The error identification data can preferably be used for the unambiguous designation of an error situation.

In one embodiment of the method for error analysis in a process engineering system, it is provided that documentation data and error correction data are stored in the error situation data record for a respective error situation.

Documentation data preferably include operating instructions, manuals, circuit diagrams, process diagrams and / or data sheets of the system parts affected by a respective error situation.

Error elimination data preferably include information for eliminating an error situation, in particular instructions for eliminating an error situation.

In particular, documentation data and troubleshooting data can also be added to the error situation data record by a user.

In one embodiment of the method for error analysis in a process engineering system, provision is made for process values to be stored synchronized with a recognized error situation during operation of the process engineering system.

In one embodiment of the method for error analysis in a process engineering system, it is provided that process values are provided with a time stamp, by means of which the process values can be clearly assigned to a point in time.

The present invention also relates to a fault analysis system for fault analysis in a process plant, for example in a paint shop, which is designed and set up to carry out the method according to the invention for fault analysis in a process plant, for example in a paint shop.

The present invention also relates to an industrial control system which comprises a fault analysis system according to the invention.

The present invention also relates to a method for predicting process deviations in a process plant, for example in a painting plant.

The present invention is based on the further object of providing a method for predicting process deviations in a process engineering system, for example in a painting system, by means of which process deviations can be easily and reliably predicted.

This object is achieved by a method for predicting process deviations in a process engineering system, for example in a painting system.

• - automatisches Erstellen eines Vorhersagemodells;
• - Vorhersage von Prozessabweichungen im Betrieb der verfahrenstechnischen Anlage unter Verwendung des Vorhersagemodells.

The method for predicting process deviations in a process plant, for example in a paint shop, preferably includes the following:

• - automatic creation of a predictive model;
• - Prediction of process deviations in the operation of the process plant using the prediction model.

A process deviation from production-critical process values can preferably be predicted by means of the prediction model.

In one embodiment of the method for predicting process deviations, it is provided that the method for predicting process deviations is carried out in an industrial air supply system, in a pretreatment station, in a station for cathodic dip painting and / or in a dryer station.

Industrial supply air systems, pretreatment stations and / or stations for cathodic dip painting are in particular very slow process engineering systems.

Production-critical process values of such process engineering systems therefore change only very slowly when they are in operation.

Due to the great inertia of process engineering systems of this type, a process deviation in the operation of the process engineering system can preferably be predicted at an early stage by means of the prediction model.

Time savings for repairing and / or maintaining such process engineering systems can thus preferably be achieved before a process deviation occurs.

An industrial air supply system preferably comprises several conditioning modules, for example a preheating module, a cooling module, a post-heating module and / or a humidifier module.

The prediction model created can preferably be transferred to similar process engineering systems.

For example, it is conceivable that a prediction model that was created for a pretreatment station can be used for a station for cathodic dip painting.

In one embodiment of the method for predicting process deviations, it is provided that the prediction model is used to predict process deviations from production-critical process values in the process plant, in particular on the basis of changing process values during operation of the process plant.

In the context of this description and the appended claims, production-critical process values are understood to mean, in particular, process values whose deviation from a predetermined process window leads to a quality deviation, in particular to quality defects.

Production-critical process values of an industrial air supply system are, for example, the temperature and the relative humidity of the air conditioned by means of the industrial air supply system, in particular at a discharge part of the industrial air supply system.

Air conditioned by means of an industrial air supply system is preferably fed to a paint booth in a paint shop and thus preferably has a direct effect on the quality of treatment of the workpieces treated in the paint booth, in particular the vehicle bodies treated in the paint booth.

For example, it is possible to use the prediction model to predict process deviations from production-critical process values for a prediction horizon of at least approximately 10 minutes, for example of at least approximately 15 minutes, preferably of at least approximately 20 minutes.

In one embodiment of the method for predicting process deviations, provision is made for process values and / or status variables to be stored over a predetermined period of time during operation of the process engineering system in order to automatically create the prediction model.

• - Zielgrößen der industriellen Zuluftanlage, insbesondere Temperatur und relative Luftfeuchte der mittels der industriellen Zuluftanlage konditionierten Luft, insbesondere an einem Ausblasteil der industriellen Zuluftanlage;
• - Stellgrößen, insbesondere Ventilstellungen von Ventilen von Heiz- und/oder Kühlmodulen der industriellen Zuluftanlage, Drehfrequenzen von Pumpen, insbesondere der Befeuchterpumpe, und/oder Drehfrequenzen von Ventilatoren;
• - interne Größen, insbesondere Vor- und/oder Rücklauftemperaturen in der Heiz- und/oder Kühlmodulen der industriellen Zuluftanlage und/oder Luftkonditionen zwischen Konditionierungsmodulen;
• - gemessene Störgrößen, insbesondere Außentemperatur und/oder relative Außenluftfeuchte an einem Einblasteil der industriellen Zuluftanlage;
• - ungemessene Störgrößen; und/oder
• - Statusgrößen, insbesondere Befeuchterpumpe (an/aus), manueller Modus für Pumpen (an/aus), Versorgungsventile (offen/geschlossen), Ventilator (an/aus).

If the process plant is an industrial air supply system, the stored process values and / or status variables preferably include the following:

• - Target variables of the industrial air intake system, in particular the temperature and relative humidity of the air conditioned by means of the industrial air intake system, in particular at a discharge part of the industrial air intake system;
• - manipulated variables, in particular valve positions of valves of heating and / or cooling modules of the industrial air supply system, rotational frequencies of pumps, in particular of the humidifier pump, and / or rotational frequencies of fans;
• - internal variables, in particular flow and / or return temperatures in the heating and / or cooling modules of the industrial air supply system and / or air conditions between conditioning modules;
• - Measured disturbance variables, in particular outside temperature and / or relative outside air humidity at a blow-in part of the industrial air supply system;
• - unmeasured disturbances; and or
• - Status variables, in particular humidifier pump (on / off), manual mode for pumps (on / off), supply valves (open / closed), fan (on / off).

In the context of this description and the appended claims, process values are understood to mean in particular time-dependent, continuous signals.

In the context of this description and the appended claims, status variables are understood to mean in particular time-dependent discrete events.

• - die verfahrenstechnische Anlage ist in dem vorgegebenen Zeitraum zu mindestens ungefähr 60 %, vorzugsweise zu mindestens ungefähr 80 %, in einem betriebsbereiten Zustand, insbesondere für einen Produktionsbetrieb;
• - die verfahrenstechnische Anlage ist in dem vorgegebenen Zeitraum zu mindestens ungefähr 60 %, vorzugsweise zu mindestens ungefähr 80 %, in einem produktionsfähigen Zustand;
• - die verfahrenstechnische Anlage wird in dem vorgegebenen Zeitraum insbesondere mit sämtlichen möglichen Betriebsstrategien betrieben;
• - einer vorgegebene Anzahl von Prozessabweichungen und/oder Störfällen in dem vorgegebenen Zeitraum.

In one embodiment of the method for predicting process deviations, it is provided that the specified period of time over which process values and / or status variables are stored during operation of the process engineering system is specified as a function of one or more of the following criteria:

• - The process engineering system is in the predetermined period of time at least approximately 60%, preferably at least approximately 80%, in an operational state, in particular for a production plant;
• - The process engineering plant is at least approximately 60%, preferably at least approximately 80%, in a productive state in the specified period;
• - The process engineering system is operated in the specified period, in particular with all possible operating strategies;
• - a specified number of process deviations and / or incidents in the specified period.

• - ein Ventilator der industriellen Zuluftanlage in Betrieb ist (Statusgröße des Ventilators ist „an“);
• - Konditionierungsmodule der industriellen Zuluftanlage in einem Automatik-Modus betrieben werden;
• - mindestens ein Regelventil geöffnet ist; und/oder
• - eine Befeuchterpumpe in Betrieb ist (Statusgröße der Befeuchterpumpe ist „an“).

If the process plant is an industrial air supply system, it is preferably in an operational state for a production facility if:

• - a fan of the industrial supply air system is in operation (status variable of the fan is "on");
• - Conditioning modules of the industrial air supply system are operated in an automatic mode;
• - At least one control valve is open; and or
• - a humidifier pump is in operation (status variable of the humidifier pump is "on").

In the context of this description and the appended claims, a production-capable state of a process-engineering system is understood in particular to mean that target variables of the process-engineering system are within a predetermined process window.

If the process engineering system is an industrial air intake system, it is in a productive state if the target variables of the industrial air intake system, in particular a temperature and relative humidity of the air conditioned by means of the industrial air intake system, in particular at a blow-out part of the industrial air intake system, are within a specified process window are located.

A pretreatment station or a station for cathodic dip painting can in particular only be operated with a single operating strategy.

An industrial air supply system can in particular be operated with several operating strategies, in particular depending on ambient conditions.

An industrial air supply system can be operated, for example, with the following operating strategies: heating-humidifying, cooling-heating, cooling-humidifying, cooling, heating, humidifying.

If the process engineering system is an industrial air supply system, it is particularly conceivable that process values and / or status variables for automatically creating the forecast model over a period of, for example, at least approximately 6 months, in particular over for a period of at least approximately 9 months, preferably for a period of at least approximately 12 months.

If the process engineering system is a pretreatment station or a station for cathodic dip painting, it is particularly conceivable that process values and / or status variables for automatically creating the prediction model over a period of, for example, at least approximately 2 weeks, in particular over a period of at least approximately 4 weeks, preferably for a period of at least about 6 weeks.

For example, it is conceivable that at least approximately 30, preferably at least approximately 50, process deviations and / or malfunctions occur in the specified period.

In particular, it is also conceivable that the predefined period of time over which process values and / or status variables are stored in the operation of the process engineering system comprises several non-contiguous partial periods.

• - eine Mindestdauer des Teilzeitraums, beispielsweise mindestens ungefähr 30 Minuten;
• - ein Betrieb der verfahrenstechnischen Anlage in einem produktionsfähigen und/oder eingeschwungenen Zustand zu Beginn des Teilzeitraums;
• - ein Betrieb der verfahrenstechnischen Anlage in einem eingeschwungenen Zustand am Ende eines Teilzeitraums.

If the period over which process values and / or status variables are stored in the operation of the process engineering plant comprises several non-contiguous partial periods, the partial periods preferably each have one or more of the following criteria:

• - a minimum duration of the partial period, for example at least approximately 30 minutes;
• - an operation of the process engineering plant in a productive and / or steady state at the beginning of the partial period;
• - Operation of the process plant in a steady state at the end of a partial period.

In one embodiment of the method for predicting process deviations, provision is made for a machine learning method to be carried out to create the prediction model, with the process values and / or status variables stored over the specified period being used to create the prediction model.

Machine learning methods carried out to automatically create the prediction model preferably include one or more of the following: Gradient Boosting, Random Forest, Support Vector Machine.

In one embodiment of the method for predicting process deviations, it is provided that the machine learning method is carried out on the basis of features that are extracted from the process values and / or status variables stored over the predetermined period.

• - statistische Kennzahlen;
• - Koeffizienten aus einer Hauptkomponentenanalyse;
• - lineare Regressionskoeffizienten;
• - dominante Frequenzen und/oder Amplituden aus dem Fourier-Spektrum.

In one embodiment of the method for predicting process deviations, it is provided that one or more of the following are used to extract the features:

• - statistical indicators;
• - coefficients from a principal component analysis;
• - linear regression coefficients;
• - dominant frequencies and / or amplitudes from the Fourier spectrum.

Statistical key figures include, for example, a minimum, a maximum, a median, a mean and / or a standard deviation.

In one embodiment of the method for predicting process deviations, it is provided that a selected number of prediction data sets with process deviations and a selected number of prediction data sets without process deviations are used for training the prediction model.

In particular, it is conceivable that the selected number of prediction data sets with process deviations corresponds at least approximately to the selected number of prediction data sets without process deviations.

In particular, it is conceivable that the selected number of prediction data sets with process deviations and the selected number of prediction data sets without process deviations are identical.

• - einem zeitlichen Mindestabstand zwischen zwei Prädiktionsdatensätzen mit Prozessabweichungen;
• - einer automatischen Auswahl anhand von definierten Regeln;
• - einer Auswahl durch einen Benutzer.

In one embodiment of the method for predicting process deviations, it is provided that the number of prediction data sets with process deviations is selected on the basis of one or more of the following criteria:

• - a minimum time interval between two prediction data sets with process deviations;
• - an automatic selection based on defined rules;
• - a selection by a user.

A minimum time interval between two prediction data sets with process deviations is, for example, at least approximately two hours.

In one embodiment of the method for predicting process deviations, it is provided that prediction data sets with process deviations are identified as such if a process deviation occurs within a predetermined time interval.

A predefined time interval preferably consists of a time span of the prediction data set and a selected prediction horizon.

For example, it is conceivable that the time span of the prediction data set is 30 minutes and that the selected prediction horizon is 15 minutes.

A prediction data set without process deviations is marked as such if there is no process deviation within the specified time interval.

In one embodiment of the method for predicting process deviations, it is provided that the process values and / or status variables stored over the specified time period are combined into prediction data sets by preprocessing.

• - Regularisierung der über den vorgegebenen Zeitraum gespeicherten Prozesswerte;
• - Zusammenfassung der Prozesswerte und/oder Statusgrößen in Prädiktionsdatensätze durch Einteilung der Prozesswerte und/oder Statusgrößen in Zeitfenster mit zeitlicher Verschiebung.

In one embodiment of the method for predicting process deviations, it is provided that the preprocessing comprises the following:

• - Regularization of the process values stored over the specified period;
• - Summary of the process values and / or status variables in prediction data sets by dividing the process values and / or status variables into time windows with a time shift.

A duration of a time window is preferably greater than the time shift.

The duration of a time window is 30 minutes, for example.

The time shift is, for example, 5 minutes.

Prediction data records that follow one another in time preferably each include process values and / or status variables with a time overlap, for example of 5 minutes.

The present invention also relates to a prediction system for predicting process deviations in a process engineering system, which is designed and set up to carry out the method according to the invention for predicting process deviations in a process engineering system, for example in a painting system.

The present invention also relates to an industrial control system comprising a prediction system according to the invention.

The method according to the invention for predicting process deviations preferably has one or more of the features and / or advantages described in connection with the method according to the invention for error analysis.

The method according to the invention for error analysis preferably also has one or more of the features and / or advantages described in connection with the method according to the invention for predicting process deviations.

The present invention also relates to a method for anomaly and / or error detection in a process engineering plant, for example in a painting plant.

The present invention is based on the further object of providing a method for anomaly and / or error detection in a process engineering plant, for example in a painting plant, by means of which anomalies and / or error situations can be easily and reliably detected.

This object is achieved by a method for anomaly and / or error detection in a process engineering system, for example in a painting system.

• - automatisches Erstellen eines Anomalie- und/oder Fehlermodells der verfahrenstechnischen Anlage, welches Informationen über die Auftretenswahrscheinlichkeit von Prozesswerten umfasst;
• - automatisches Einlesen von Prozesswerten der verfahrenstechnischen Anlage im Betrieb derselben;
• - automatische Erkennung einer Anomalie und/oder einer Fehlersituation durch Ermitteln einer Auftretenswahrscheinlichkeit mittels des Anomalie- und/oder Fehlermodells auf Basis der eingelesenen Prozesswerte der verfahrenstechnischen Anlage und durch Überprüfen der Auftretenswahrscheinlichkeit auf einen Grenzwert.

The method for anomaly and / or error detection in a process plant, for example in a paint shop, preferably includes the following:

• - Automatic creation of an anomaly and / or error model of the process engineering system, which includes information about the probability of occurrence of process values;
• - Automatic reading in of process values of the process engineering system during operation of the same;
• - Automatic detection of an anomaly and / or an error situation by determining a probability of occurrence by means of the anomaly and / or error model on the basis of the read-in process values of the procedural plant and by checking the probability of occurrence for a limit value.

Preferably, by means of the method for anomaly and / or error detection, error situations, that is to say defects and / or failures in components, sensors and / or actuators, can be identified.

With the method for anomaly and / or error detection in a process engineering system, a normal state of the process engineering system can preferably be determined in an automated manner.

In particular, static and / or dynamic relationships in the process engineering system can be described by means of the anomaly and / or error model.

In the context of this description and the appended claims, an anomaly is understood to mean, in particular, a deviation of a process value from a normal state.

The anomaly and / or error model preferably includes a structure graph.

The structure graph comprises in particular a plurality of cliques, relationships between nodes of a respective clique preferably being described by a probability density function.

In each case, a clique of the structure graph is used to describe relationships for sensors and / or actuators of the process engineering system.

An anomaly is preferably recognized when a limit value for the probability of occurrence of a process value in a clique of a structure graph of the anomaly and / or error model is not reached.

It can be beneficial if a recognized anomaly is graphically represented with anomalous process variables for a user.

• - das Anomalie- und/oder Fehlermodell Strukturdaten umfasst, welche Informationen über eine Prozessstruktur in der verfahrenstechnischen Anlage enthalten, und/oder dass
• - das Anomalie- und/oder Fehlermodell Parametrierungsdaten umfasst, welche Informationen über Zusammenhänge zwischen Prozesswerten der verfahrenstechnischen Anlage enthalten.

In one embodiment of the method for anomaly and / or error detection, it is provided that

• the anomaly and / or error model comprises structural data which contain information about a process structure in the process engineering plant, and / or that
• - The anomaly and / or error model comprises parameterization data which contain information about relationships between process values of the process engineering plant.

The structural data include, in particular, information about relationships between sensors and / or actuators in the process engineering system.

The parameterization data include in particular information about the probability of occurrence of process values.

In particular, structural data and / or parameterization data are used to create the anomaly and / or error model.

• - Strukturidentifikation zur Ermittlung einer Prozessstruktur der verfahrenstechnischen Anlage;
• - Ermittlung von Kausalitäten in der ermittelten Prozessstruktur der verfahrenstechnischen Anlage;
• - Strukturparametrierung der Zusammenhänge in der ermittelten Prozessstruktur der verfahrenstechnischen Anlage.

In one embodiment of the method for anomaly and / or error detection, it is provided that one or more of the following steps are carried out to create the anomaly and / or error model:

• - Structure identification to determine a process structure of the process plant;
• - Determination of causalities in the determined process structure of the procedural plant;
• - Structural parameterization of the relationships in the determined process structure of the process plant.

The anomaly and / or error model preferably includes structural information, causality information and / or structural parameterization information.

The structure parameterization can preferably be facilitated by means of the structure identification.

In particular, by means of the structure identification, a parameterization effort and thus in particular a computing effort for the structure parameterization can be reduced.

In one embodiment of the method for anomaly and / or error detection, it is provided that a structure graph is determined during the structure identification to determine a process structure of the process engineering system, which in particular depicts relationships in the process engineering system.

The structure graph preferably comprises several nodes and several edges that connect the nodes to one another in pairs.

The structure graph preferably comprises several cliques.

It can be favorable if relationships are determined in the determined structure graph by means of the structure identification.

• - eines maschinellen Lernverfahrens;
• - Expertenwissen;
• - bekannten Schaltplänen und/oder Verfahrensschemata;
• - Bezeichnungen in einem Nummerierungssystem der verfahrenstechnischen Anlage.

In one embodiment of the method for anomaly and / or error detection provides that the structure graph is determined using one or more of the following:

• - a machine learning process;
• - expert knowledge;
• - known circuit diagrams and / or process schemes;
• - Designations in a numbering system of the process plant.

The determination of the structure graph by means of a machine learning method is preferably carried out using correlation measures by means of which non-linear correlations can be reproduced, for example by means of transinformation (“mutual information”).

In the context of this description and the appended claims, expert knowledge is understood to mean, for example, knowledge about relationships between sensors in the process.

Edges between nodes of the structure graph can preferably be excluded by a pre-configuration of the structure graph using information from expert knowledge, known circuit diagrams and / or process schemes. In particular, a computational effort for determining the structure graph can be reduced.

Process values are preferably clearly identified by means of the numbering system (“plant numbering system”).

It can therefore be beneficial if the structure graph is determined using the unique designation of the process values.

In particular, it is conceivable that the structure graph determined by means of a machine learning process is checked for plausibility using expert knowledge, using known circuit diagrams and / or process schemes and / or using the designations in the numbering system of the process engineering plant.

In an embodiment of the method for anomaly and / or error detection, it is provided that the procedural system for structure identification, in particular for determining the structure graph, is stimulated with test signals.

Anomalies and / or error situations are preferably generated in a targeted manner during the excitation with test signals.

Test signals are generated in particular taking design data into account. In particular, limits for the test signals can be specified on the basis of the design data, for example a maximum amplitude of the manipulated variable jumps can be specified when specifying step functions.

• - Sensortyp (Temperatursensor, Durchflusssensor, Ventilstellungssensor, Drucksensor etc.) und/oder Aktortyp (Ventil, Ventilator, Klappe, E-Motor);
• - zulässige Wertebereiche von Sensoren und/oder Aktoren;
• - Signaltyp von Sensor und/oder Aktor (float, integer).

In the context of this description and the attached claims, design data is understood to mean in particular one or more of the following information:

• - Sensor type (temperature sensor, flow sensor, valve position sensor, pressure sensor etc.) and / or actuator type (valve, fan, flap, electric motor);
• - Permissible value ranges of sensors and / or actuators;
• - Signal type from sensor and / or actuator (float, integer).

In particular, the process engineering system is dynamically excited by means of the test signals.

The test signals are, in particular, signals by means of which manipulated variables can be changed in the process engineering system. For example, manipulated variables of valves and / or pumps of the process engineering system are changed by means of the test signals.

• - bei der Anregung der verfahrenstechnischen Anlage mit Testsignalen generierte Systemeingangssignale und Systemausgangssignale;
• - Expertenwissen;
• - bekannte Schaltpläne und/oder Verfahrensschemata;
• - Bezeichnungen in einem Nummerierungssystem der verfahrenstechnischen Anlage.

In one embodiment of the method for anomaly and / or error detection, it is provided that the determination of causalities in the determined process structure of the procedural plant takes place using one or more of the following:

• - System input signals and system output signals generated when the process plant is excited with test signals;
• - expert knowledge;
• - known circuit diagrams and / or process schemes;
• - Designations in a numbering system of the process plant.

Causalities in the ascertained process structure are derived, for example, from system input signals and system output signals of the process engineering system ascertained when the process engineering system is excited with test signals, for example based on the respective temporal course of the system input signals and the system output signals.

As an alternative or in addition to this, it is conceivable that causalities from system input signals and system input signals determined when the process engineering system is excited with test signals System output signals are derived by means of methods for causal inference.

In the context of this description and the appended claims, causalities are understood to mean, in particular, directions of causality, that is to say “directions of arrows”, in the structure graph determined.

Preferably, by means of the determined causalities in the determined process structure or in the determined structure graph, the process values that are the cause of a recognized anomaly can be found.

• - Verfahren zur Ermittlung von Wahrscheinlichkeitsdichtefunktionen, insbesondere Gaußsche Mischmodelle;
• - bekannte physikalische Zusammenhänge zwischen Prozesswerten;
• - physikalische Kennfelder von Funktionselementen der verfahrenstechnischen Anlage, beispielsweise Kennfelder von Ventilen.

In one embodiment of the method for anomaly and / or error detection, it is provided that one or more of the following are used for the structural parameterization of the relationships in the ascertained process structure of the procedural plant:

• - Method for determining probability density functions, in particular Gaussian mixed models;
• - known physical relationships between process values;
• - physical maps of functional elements of the process engineering system, for example maps of valves.

The structure parameterization is preferably carried out using methods for determining probability density functions, in particular using Gaussian mixed models.

It can be beneficial if relationships between two variables of the functional element can be described by means of physical characteristics maps of functional elements of the process engineering system.

A relationship between a valve position and a volume flow can, for example, be described by means of a known valve characteristics map of a valve.

In one embodiment of the method for anomaly and / or error detection, it is provided that, for structural parameterization using methods for determining probability density functions, in particular using Gaussian mixed models, data from the regular operation of the process engineering system and / or through an excitation of the process engineering system data obtained by means of test signals are used.

For example, manipulated, measured and / or controlled variables stored in a database are used for structure parameterization using methods for determining probability density functions.

For structural parameterization using methods for determining probability density functions, data from the ongoing operation of the process engineering system are preferably used, which are stored over a period of at least 2 weeks, preferably at least 4 weeks, for example at least 8 weeks.

In one embodiment of the method for anomaly and / or error detection, it is provided that the data used for structural parameterization using methods for determining probability density functions, in particular using Gaussian mixed models, are preprocessed prior to structural parameterization.

During preprocessing, data from the regular operation of the plant are preferably excluded using alarms and status bits that describe the state of the process plant, which are not assigned to operational or production-ready operating states of the process plant (e.g. shut down plant, maintenance phases, etc.).

It can also be beneficial if data from the regular operation of the system are preprocessed by filtering, for example by means of low-pass filters and / or by means of Butterworth filters.

Data from regular operation are preferably interpolated to a uniform time step size.

In one embodiment of the method for anomaly and / or error detection, it is provided that when creating the anomaly and / or error model, a limit value for the probability of occurrence of a process value is established, an anomaly being detected if the limit value is not reached.

The limit value is preferably established using a non-linear optimization method, for example using the Nelder-Mead method.

As an alternative or in addition to this, it is conceivable that the limit value is determined using quantiles.

Limit values for the probability of occurrence of the process values can preferably be optimized, for example, by specifying a "false positive rate".

The limit values are preferably adapted after the anomaly and / or error model has been created for the first time, in particular in the case of an excessive number of false alarms.

In one embodiment of the method for anomaly and / or error detection, it is provided that the method for anomaly and / or error detection is used to identify a cause of a detected anomaly and / or a detected error situation.

In particular, the cause of the error can be identified by means of the structure graph of the anomaly and / or error model.

The structure graph is preferably visualized for a user to identify the anomaly and / or the error situation and / or to identify the cause of the error.

In particular, a “root cause analysis” can be implemented using the structure graph. In particular, abnormal process values can be identified within a process structure of the procedural plant.

A recognized anomaly can preferably be identified by a user as an error situation or false alarm.

Error situations are stored, in particular, in an error database.

• - Vorbehandlungsstation;
• - Station zur kathodischen Tauchlackierung;
• - Trocknerstationen;
• - industrielle Zuluftanlage;
• - Lackierroboter.

In one embodiment of the method for anomaly and / or error detection, it is provided that the procedural system comprises one or more of the following treatment stations of a painting system or is formed by them:

• - pre-treatment station;
• - Station for cathodic dip painting;
• - dryer stations;
• - industrial air supply system;
• - painting robots.

The present invention also relates to an anomaly and / or error detection system for anomaly and / or error detection, which is designed and set up to carry out the method according to the invention for anomaly and / or error detection in a process engineering system, for example in a paint shop.

The anomaly and / or error detection system forms, in particular, a reporting system by means of which an error situation in the process engineering system can be automatically identified.

The present invention also relates to an industrial control system which comprises an anomaly and / or error detection system according to the invention.

The method according to the invention for anomaly and / or error detection preferably has one or more of the features and / or advantages described in connection with the method according to the invention for error analysis and / or the method according to the invention for predicting process deviations.

The inventive method for error analysis and / or the inventive method for predicting process deviations preferably have one or more of the features and / or advantages described in connection with the inventive method for anomaly and / or error detection.

Further features and / or advantages of the invention are the subject matter of the following description and the graphic representation of exemplary embodiments.

• 1 eine schematische Darstellung einer verfahrenstechnischen Anlage und eines industriellen Steuerungssystems;
• 2 eine schematische Darstellung einer verfahrenstechnischen Anlage, insbesondere einer Lackieranlage;
• 3 eine schematische Darstellung einer industriellen Zuluftanlage;
• 4 die schematische Darstellung der industriellen Zuluftanlage aus 3 beim Auftreten einer Fehlersituation;
• 5 die schematische Darstellung der industriellen Zuluftanlage aus 3 beim Auftreten einer weiteren Fehlersituation;
• 6 die schematische Darstellung der industriellen Zuluftanlage aus 3 beim Auftreten einer weiteren Fehlersituation;
• 7 eine weitere schematische Darstellung einer industriellen Zuluftanlage;
• 8 die schematische Darstellung der industriellen Zuluftanlage aus 7 in einem Betriebszustand ohne Prozessabweichung;
• 9 die schematische Darstellung der industriellen Zuluftanlage aus 7 in einem Betriebszustand mit einer Prozessabweichung aufgrund von sich ändernden Umgebungsbedingungen;
• 10 die schematische Darstellung der industriellen Zuluftanlage aus 7 in einem Betriebszustand mit einer Prozessabweichung aufgrund des Einschaltens eines Wärmerückgewinnungssystems;
• 11 die schematische Darstellung der industriellen Zuluftanlage aus 7 in einem Betriebszustand mit einer Prozessabweichung aufgrund des Ausfalls eines Ventils;
• 12 eine schematische Darstellung von in Prädiktionsdatensätze zusammengefassten Prozesswerten;
• 13 eine schematische Darstellung der Prädiktionsdatensätze aus 12, welche als Prädiktionsdatensätze mit Prozessabweichungen und als Prädiktionsdatensätze ohne Prozessabweichung gekennzeichnet sind;
• 14 eine schematische Darstellung einer Vorbehandlungsstation;
• 15 eine schematische Darstellung von Verfahrensschritten zur Erstellung eines Anomalie- und/oder Fehlermodells der Vorbehandl ungsstation;
• 16 eine schematische Darstellung eines Graphen mit einer aus der Vorbehandlungsstation aus 14 abgeleiteten Prozessstruktur;
• 17 eine Clique eines Faktorgraphen;
• 18 ein Modell eines funktionalen Zusammenhangs in der Clique aus 17; und
• 19 eine der Clique aus 17 entsprechende Clique, welche durch Zuweisung einer Fehlerursache um einen Knoten erweitert ist.

In the drawings show:

• 1 a schematic representation of a process plant and an industrial control system;
• 2 a schematic representation of a process plant, in particular a paint shop;
• 3 a schematic representation of an industrial air supply system;
• 4th the schematic representation of the industrial air supply system 3 when an error situation occurs;
• 5 the schematic representation of the industrial air supply system 3 when another error situation occurs;
• 6th the schematic representation of the industrial air supply system 3 when another error situation occurs;
• 7th another schematic representation of an industrial air supply system;
• 8th the schematic representation of the industrial air supply system 7th in an operating state without process deviation;
• 9 the schematic representation of the industrial air supply system 7th in one Operating status with a process deviation due to changing environmental conditions;
• 10 the schematic representation of the industrial air supply system 7th in an operating state with a process deviation due to the activation of a heat recovery system;
• 11 the schematic representation of the industrial air supply system 7th in an operating state with a process deviation due to the failure of a valve;
• 12 a schematic representation of process values summarized in prediction data sets;
• 13 a schematic representation of the prediction data sets 12 , which are identified as prediction data sets with process deviations and as prediction data sets without process deviations;
• 14th a schematic representation of a pre-treatment station;
• 15th a schematic representation of method steps for creating an anomaly and / or failure model of the pretreatment station;
• 16 a schematic representation of a graph with one from the pre-treatment station 14th derived process structure;
• 17th a clique of a factor graph;
• 18th a model of a functional relationship in the clique 17th ; and
• 19th one of the clique 17th corresponding clique, which is expanded by a node by assigning a cause of the error.

Identical or functionally equivalent elements are provided with the same reference symbols in all figures.

1 shows an industrial control system, designated as a whole by 100, for a process engineering plant 101.

The process engineering system 101 is, for example, a painting system 104, which in particular is shown in 2 is shown.

Based on 1 to 6th In particular, a method for error analysis in the process engineering system 101, in particular in the painting system 102, is explained.

Based on 1 , 2 and 7th to 13 In particular, a method for predicting process deviations in the process engineering system 101, in particular in the painting system 102, is explained.

Based on 1 , 2 and 14th to 19th In particular, a method for anomaly and / or error detection in the process engineering system 101, in particular in the painting system 102, is explained.

In the 2 The process engineering system 101 shown, in particular the painting system 102, preferably comprises several treatment stations 104 for treating workpieces 106, in particular for treating vehicle bodies 108.

The treatment stations 104 are at the in 2 The illustrated embodiment of the painting installation 102 is in particular linked to one another and therefore forms a painting line 110.

In order to treat workpieces 106, in particular to paint vehicle bodies 108, the workpieces 106 pass through the treatment stations 104, preferably one after the other.

For example, it is conceivable that a workpiece 106 successively passes through subsequent treatment stations 104 in the specified order.

A workpiece 106 is pretreated in a pretreatment station 112 and conveyed from the pretreatment station 112 to a station for cathodic dip painting 114.

From the cathodic dip painting station 114, after a coating has been applied to the workpiece 106, it is conveyed to a dryer station 116 after the cathodic dip painting station 114.

After the coating applied to the workpiece 106 in the cathodic dip painting station 114 has dried in the dryer station 116, the workpiece 106 is preferably conveyed into a base coat booth 118, in which a coating is in turn applied to the workpiece 106.

After the coating has been applied in the base coat booth 118, the workpiece 106 is preferably conveyed to a base coat drying station 120.

After the coating applied to the workpiece 106 in the base-coat booth 118 has dried in the base-coat drying station 120, the workpiece 106 is preferably conveyed into a clear-coat booth 122 in which a further coating is applied to the workpiece 106.

After the coating has been applied in the clear coat booth 122, the workpiece 106 is preferably fed to a clear coat drying station 124.

After the coating applied to the workpiece 106 in the clear coat booth 122 has been dried in the clear coat drying station 124, the workpiece 106 is preferably fed to a control station 126 at the end of the production process.

In the control station 126, a quality control is preferably carried out by a quality inspector, for example by means of a visual inspection.

The process engineering system 101, in particular the painting system 102, preferably further comprises an industrial air supply system 128 for conditioning the air, which is supplied, for example, to the base coat booth 118 and / or the clear coat booth 122.

A temperature and / or a relative humidity of the air that is supplied to the base coat cabin 118 and / or the clear coat cabin 122 can preferably be set by means of the industrial air supply system 128.

By means of the industrial control system 100, a production process, in particular a painting process, can preferably be controlled in treatment stations 104 of the process engineering system 101, in particular the painting system 102.

For this purpose, the industrial control system 100 preferably comprises a process control system 130.

This in 1 The illustrated industrial control system 100 preferably further comprises a database 132.

The database 132 of the industrial control system 100 preferably comprises a process database 134 and a fault database 136.

It can also be favorable if the industrial control system 100 comprises a reporting system 138 and an analysis system 140.

The industrial control system 100 preferably further comprises a visualization system 142, by means of which information can be visualized for a user.

The visualization system 142 preferably comprises one or more screens on which information can be displayed.

The analysis system 140 preferably comprises or is formed by a fault analysis system 144.

It can also be favorable if the reporting system 138 comprises a prediction system 146 for predicting process deviations in the process engineering plant 101 or is formed by this.

As an alternative or in addition to this, it is conceivable that the reporting system 138 includes an anomaly and / or error detection system 148.

The fault analysis system 144 is set up and designed in particular to use the 1 to 6th to carry out the explained method for error analysis in the process engineering system 101.

The prediction system 146 is set up and designed in particular to use the 1 , 2 and 7th to 13 to carry out the explained methods for predicting process deviations in the process plant 101.

The anomaly and / or error detection system 148 is designed in particular to use the 1 , 2 and 14th to 19th to carry out the explained methods for anomaly and / or error detection in the procedural plant 101.

The ones in the 3 to 6th The illustrated industrial air supply system 128 preferably comprises a plurality of conditioning modules 150, for example a preheating module 154, a cooling module 156, a post-heating module 158 and / or a humidifier module 160.

For example, the industrial air supply system 128 of the painting system 102 is a functional unit, whereby a conditioning module 150 of the air supply system 128 is a functional group and a circulation pump 152 of the air supply system is a functional element (cf. 3 to 6th ).

In addition to the circulation pumps 152 of the preheating module, the cooling module 156 and the post-heating module 158, the industrial air supply system 128 preferably further comprises a humidifier pump 153 of the humidifier module 160.

It can also be favorable if the air supply system 128 includes a fan 162.

The supply air system 128 preferably further comprises a heat recovery system 164 for heat recovery.

An air stream 165 can preferably be fed to the air supply system 128 from its surroundings.

An air flow 167 conditioned by means of the supply air system 128 can preferably be fed to the base coat cabin 118 and / or the clear coat cabin 122.

The air supply system preferably comprises sensors, not shown in the drawings, by means of which process values can be recorded.

• - Außentemperatur 166;
• - Außenfeuchte 168;
• - Temperatur der mittels der industriellen Zuluftanlage konditionierten Luft 170;
• - Feuchte der mittels der industriellen Zuluftanlage konditionierten Luft 172;
• - Volumenströme 174, 176, 178 in den Konditionierungsmodulen 150;
• - Ventilstellungen 180, 182, 184 von Ventilen 181, 183, 185 in den Konditionierungsmodulen 150.

For example, the following process values can be recorded using the sensors, which are stored in the 3 to 6th are preferably each identified by a reference symbol:

• - outside temperature 166;
• - external humidity 168;
• The temperature of the air 170 conditioned by means of the industrial air supply system;
• The humidity of the air 172 conditioned by means of the industrial air supply system;
• - Volume flows 174, 176, 178 in the conditioning modules 150;
• Valve positions 180, 182, 184 of valves 181, 183, 185 in the conditioning modules 150.

It can also be favorable if a rotational frequency 193 of the humidifier pump 153 and a rotational frequency 195 of the fan 194 are recorded.

The process values 166 to 184 are preferably stored in the process database 134.

• - Pumpenstatus 186, 188, 190 der Umlaufpumpen 152 der Konditionierungsmodule 150 und Pumpenstatus 192 der Befeuchterpumpe 153 (an/aus);
• - Ventilatorstatus 194 (an/aus);
• - Ventilstatus 196, 198, 200 der Konditionierungsmodule 150 (geschlossen/geöffnet) ;
• - Status 202 des Wärmerückgewinnungssystems 164 (an/aus).

It can also be provided that the following status variables are recorded, which are in the 3 to 6th are preferably also identified by a reference symbol:

• Pump status 186, 188, 190 of the circulation pumps 152 of the conditioning modules 150 and pump status 192 of the humidifier pump 153 (on / off);
• - fan status 194 (on / off);
• Valve status 196, 198, 200 of the conditioning modules 150 (closed / open);
• - Status 202 of the heat recovery system 164 (on / off).

The status variables 186 to 202 are preferably also stored in the process database 134.

The method for error analysis in the procedural plant 101 is now preferably described with reference to 3 to 6th explained.

The industrial air supply system 128 in particular forms the process engineering system 101.

In the following, various example situations are described from which the functioning of the fault analysis system 144 can be seen.

Example situation 1 (see Fig. 4):

(Valve leakage)

A valve leak occurs on the preheat module 154. A volume flow 174> 0 is measured. The pump status 186 is "off" and the valve status 196 of the control valve is "closed".

The error situation is stored in the message system as logic (pump status 186 = "off"; volume flow 174> 0 and valve status 196 "closed"). The message system has therefore preferably saved the process and status values as a pre-link.

1. 1) Die Meldung wird einem Benutzer mittels des Visualisierungssystems 142, beispielsweise mittels eines Bildschirms des Visualisierungssystems 142, angezeigt.
2. 2) Ein Benutzer möchte die Situation analysieren und öffnet ein Diagnosefenster.
3. 3) Im Diagnosefenster werden dem Benutzer der mit der Meldung vorverknüpfte Prozesswert 174 sowie die Statusgrößen 186 und 196 angezeigt. Das Fehleranalysesystem 144 bekommt diese Information vorzugsweise direkt aus dem Meldesystem 138.
4. 4) Eine Priorisierung der mit der Fehlersituation verknüpften Prozesswerte wird vorzugsweise nicht vorgenommen, da der Fehlersituation nur der Prozesswert 174 zugeordnet ist.
5. 5) Der Benutzer speichert die Fehlersituation mit der Verknüpfung in der Fehlerdatenbank 136 ab.
6. 6) Bei erneutem Auftreten der Fehlersituation „Ventilleckage“ an dem gleichen oder vergleichbaren Ventil wird dem Benutzer vorzugsweise die vergleichbare Fehlersituation angezeigt.

The error analysis steps when a message occurs due to valve leakage are preferably as follows:

1. 1) The message is displayed to a user by means of the visualization system 142, for example by means of a screen of the visualization system 142.
2. 2) A user wants to analyze the situation and opens a diagnostic window.
3. 3) In the diagnostic window, the user is shown the process value 174 linked to the message and the status variables 186 and 196. The fault analysis system 144 preferably receives this information directly from the reporting system 138.
4. 4) The process values linked to the error situation are preferably not prioritized since only the process value 174 is assigned to the error situation.
5. 5) The user saves the error situation with the link in the error database 136.
6. 6) If the error situation “valve leakage” occurs again on the same or comparable valve, the user is preferably shown the comparable error situation.

Example situation 2 (see Fig. 5):

(Exceeding the temperature 170 of the air conditioned by means of the industrial air supply system 128 due to an excessively high outside temperature 166)

The temperature 170 of the air conditioned by means of the industrial air supply system 128 is exceeded because the outside temperature 166 is outside a design window of the industrial air supply system 128.

The message is generated when the temperature 170 of the air conditioned by means of the industrial supply air system 128 leaves a predetermined process window and is reported by the message system 138 to the visualization system 142.

The message is linked to the value of the temperature 170 of the air conditioned by means of the industrial supply air system 128. However, the message is not linked to the outside temperature 166.

1. 1) Die Meldung wird einem Benutzer mittels des Visualisierungssystems 142, beispielsweise mittels eines Bildschirms des Visualisierungssystems 142, angezeigt.
2. 2) Ein Benutzer möchte die Situation analysieren und öffnet ein Diagnosefenster.
3. 3) Im Diagnosefenster wird dem Benutzer der mit der Meldung verknüpfte Prozesswert 170 angezeigt.
4. 4) Eine Priorisierung der mit der Fehlersituation verknüpften Prozesswerte wird vorzugsweise nicht vorgenommen, da der Fehlersituation nur der Prozesswert 166 zugeordnet ist.
5. 5) Zusätzlich werden dem Benutzer vorzugsweise folgende Prozesswerte vorgeschlagen:
   • - Feuchte der mittels der industriellen Zuluftanlage konditionierten Luft 172 (prozesskritische Größe, zeigt Anomalie im Verhalten);
   • - Außentemperatur 166 (zeigt Anomalie im Verhalten);
   • - Außenfeuchte 168 (zeigt Anomalie im Verhalten).
6. 6) Der Benutzer wählt die vorgeschlagenen Prozesswerte aus und fügt sie der Fehlersituation hinzu.
7. 7) Der Benutzer kann die Ursache der Temperaturabweichung aus dem Analysesystem 140, insbesondere aus dem Fehleranalysesystem 144, direkt erfassen, da ihm die relevanten Prozesswerte vorgeschlagen werden.
8. 8) Der Benutzer fügt eine Dokumentation zu der Fehlersituation mit einem Fehlerbehebungsvorschlag hinzu.
9. 9) Der Benutzer speichert die Fehlersituation mit der Verknüpfung und den Dokumenten in der Fehlerdatenbank ab.
10. 10) Das Fehleranalysesystem 144 erfasst vorzugsweise neben einer Fehler-ID, einer Fehlerklassifikation (Temperaturerhöhung), einem Fehlerort (Ausblasteil der industriellen Zuluftanlage 128) auch Referenzen (IDs) der Prozessgrößen 170, 172, 166, 168 in der priorisierten Reihenfolge und eine Menge von Merkmalen (z.B. Mittelwerte, Minimum, Maximum, Streuung der Prozessgrößen während des Auftretens der Fehlersituation) sowie die Zeitdauer vom Auftreten der Fehlersituation bis zum Speicherzeitpunkt beziehungsweise Ende der Fehlersituation.

The error analysis steps when the message occurs due to the temperature change are preferably as follows:

1. 1) The message is displayed to a user by means of the visualization system 142, for example by means of a screen of the visualization system 142.
2. 2) A user wants to analyze the situation and opens a diagnostic window.
3. 3) The process value 170 linked to the message is displayed to the user in the diagnostic window.
4. 4) The process values linked to the error situation are preferably not prioritized, since only the process value 166 is assigned to the error situation.
5. 5) In addition, the following process values are suggested to the user:
   • Humidity of the air 172 conditioned by means of the industrial air supply system (process-critical variable, shows anomaly in behavior);
   • - outside temperature 166 (shows anomaly in behavior);
   • - Outside humidity 168 (shows anomaly in behavior).
6. 6) The user selects the proposed process values and adds them to the error situation.
7. 7) The user can directly record the cause of the temperature deviation from the analysis system 140, in particular from the error analysis system 144, since the relevant process values are suggested to him.
8. 8) The user adds documentation on the error situation with a suggested remedy.
9. 9) The user saves the error situation with the link and the documents in the error database.
10. 10) In addition to an error ID, an error classification (temperature increase), an error location (exhaust part of the industrial air intake system 128), the error analysis system 144 also records references (IDs) of the process variables 170, 172, 166, 168 in the prioritized order and a quantity of Characteristics (e.g. mean values, minimum, maximum, scatter of the process variables during the occurrence of the error situation) as well as the time from the occurrence of the error situation to the time of storage or the end of the error situation

Example situation 3 (see Fig. 5):

(Exceeding the temperature 170 of the air conditioned by means of the industrial supply air system due to an outside temperature that is too high)

The temperature 170 of the air conditioned by means of the industrial supply air system is exceeded again due to the outside temperature. The error pattern is similar to example situation 2.

1. 1) Die Meldung wird einem Benutzer mittels des Visualisierungssystems 142, beispielsweise mittels eines Bildschirms des Visualisierungssystems 142, angezeigt.
2. 2) Der Benutzer möchte die Fehlersituation analysieren und öffnet ein Diagnosefenster.
3. 3) Im Diagnosefenster werden dem Benutzer die mit der Meldung verknüpften Prozesswerte: 170, 172, 166, 168 in beschriebener Reihenfolge angezeigt.
4. 4) Eine Prozessliste und deren Priorisierung ergeben sich aus einer ähnlichen Fehlersituation. Die Ähnlichkeit zur Fehlersituation aus Beispielsituation 2 wird vom Fehleranalysesystem 144 über einen metrischen Abgleich der Prozesswerte bestimmt.
5. 5) Die ähnliche Fehlersituation wird dem Benutzer angezeigt
6. 6) Der Benutzer kann die Verläufe der Prozesswerte in der gezeigten priorisierten Reihenfolge sowie die Dokumente der ihm gezeigten ähnlichen Fehlersituation zur Lösungsfindung nutzen.
7. 7) Der Benutzer speichert die Fehlersituation ab.

The analysis steps when the message due to the temperature change occurs are preferably as follows:

1. 1) The message is displayed to a user by means of the visualization system 142, for example by means of a screen of the visualization system 142.
2. 2) The user wants to analyze the error situation and opens a diagnosis window.
3. 3) In the diagnostic window, the process values linked to the message: 170, 172, 166, 168 are displayed to the user in the order described.
4. 4) A process list and its prioritization result from a similar error situation. The similarity to the error situation from example situation 2 is determined by the error analysis system 144 via a metric comparison of the process values.
5. 5) The similar error situation is displayed to the user
6. 6) The user can view the courses of the process values in the prioritized order shown as well as the documents of the similar error situation shown to him to find a solution.
7. 7) The user saves the error situation.

Example situation 4 (see Fig. 6):

(Disruption in a supply system)

Too little fuel gas is supplied to a burner in the preheating module 154. The volume flow 174 decreases.

In order to obtain more fuel gas, the valve 181 of the preheating module 154 is opened further, the valve position 180 changes.

The valve 185 of the post-heating module 158 also opens in order to compensate for the malfunction of the pre-heating module 154. The valve position 184 changes.

The disturbance cannot be compensated for due to the low outside temperature 166 and the temperature 170 of the air conditioned by means of the industrial air supply system collapses.

1. 1) Die Meldung wird einem Benutzer mittels des Visualisierungssystems 142, beispielsweise mittels eines Bildschirms des Visualisierungssystems 142, angezeigt.
2. 2) Der Benutzer möchte die Fehlersituation analysieren und öffnet ein Diagnosefenster.
3. 3) Im Diagnosefenster wird dem Benutzer aufgrund der Vorpriorisierung im Meldesystem 138 der mit der Meldung verknüpfte Prozesswert 170 angezeigt.
4. 4) Eine Priorisierung findet nicht statt.
5. 5) Es werden folgende Prozesswerte vorgeschlagen:
   • - Feuchte der mittels der industriellen Zuluftanlage konditionierten Luft 172 (prozesskritische Größe, zeigt Anomalie im Verhalten);
   • - Volumenstrom 174, Ventilstellung 180, Ventilstellung 186 (abhängig von der Abweichung vom Normalzustand).
6. 6) Die Fehlersituationen aus den Beispielsituationen 2 und 3 werden nicht als ähnlich eingestuft (unterschiedliches Signalverhalten, da hoher metrischer Abstand der Prozesswerte).
7. 7) Die vorgeschlagenen Prozesswerte können der Fehlersituation hinzugefügt und in der Fehlerdatenbank abgespeichert werden.

The analysis steps when the message occurs due to the malfunction in the supply system are preferably as follows:

1. 1) The message is displayed to a user by means of the visualization system 142, for example by means of a screen of the visualization system 142.
2. 2) The user wants to analyze the error situation and opens a diagnosis window.
3. 3) In the diagnosis window, the process value 170 linked to the message is displayed to the user due to the pre-prioritization in the message system 138.
4. 4) There is no prioritization.
5. 5) The following process values are suggested:
   • Humidity of the air 172 conditioned by means of the industrial air supply system (process-critical variable, shows anomaly in behavior);
   • - Volume flow 174, valve position 180, valve position 186 (depending on the deviation from the normal state).
6. 6) The error situations from example situations 2 and 3 are not classified as similar (different signal behavior, as there is a high metric distance between the process values).
7. 7) The proposed process values can be added to the error situation and saved in the error database.

The method for predicting process deviations in the process plant 101 is now preferably described with reference to FIG 7th to 13 explained.

• - Zielgrößen 204 der industriellen Zuluftanlage 128, insbesondere Temperatur 170 und relative Luftfeuchte 172 der mittels der industriellen Zuluftanlage 128 konditionierten Luft, insbesondere an einem Ausblasteil der industriellen Zuluftanlage 128;
• - Stellgrößen 206, insbesondere Ventilstellungen 180, 182, 184 von Ventilen von Heiz- und/oder Kühlmodulen 154, 156, 158 der industriellen Zuluftanlage 128, Drehfrequenzen 193 von Pumpen 152, insbesondere der Befeuchterpumpe 153, und/oder Drehfrequenzen 195 von Ventilatoren 162;
• - interne Größen 208, insbesondere Vor- und/oder Rücklauftemperaturen 210 in den Heiz- und/oder Kühlmodulen 154, 156, 158 der industriellen Zuluftanlage 128 und/oder Luftkonditionen zwischen Konditionierungsmodulen 150;
• - gemessene Störgrößen 210, insbesondere Außentemperatur 166 und/oder relative Außenluftfeuchte 168 an einem Einblasteil der industriellen Zuluftanlage 128;
• - ungemessene Störgrößen 212; und/oder
• - Statusgrößen 214, insbesondere Befeuchterpumpe 153 (an/aus); manueller Modus für Pumpen 152, 153 (an/aus); Versorgungsventile 181, 183, 185, Ventilator 162 (an/aus).

If the process plant 101 is an industrial air supply system 128, stored process values and / or status variables preferably include the following (cf. 7th ):

• Target variables 204 of the industrial air intake system 128, in particular temperature 170 and relative humidity 172 of the air conditioned by means of the industrial air intake system 128, in particular at a discharge part of the industrial air intake system 128;
• - manipulated variables 206, in particular valve positions 180, 182, 184 of valves of heating and / or cooling modules 154, 156, 158 of the industrial air intake system 128, rotational frequencies 193 of pumps 152, in particular of humidifier pump 153, and / or rotational frequencies 195 of fans 162;
• - Internal variables 208, in particular flow and / or return temperatures 210 in the heating and / or cooling modules 154, 156, 158 of the industrial air supply system 128 and / or air conditions between conditioning modules 150;
• - Measured disturbance variables 210, in particular outside temperature 166 and / or relative outside air humidity 168 at an inlet part of the industrial air supply system 128;
• - unmeasured disturbance variables 212; and or
• - Status variables 214, in particular humidifier pump 153 (on / off); manual mode for pumps 152, 153 (on / off); Supply valves 181, 183, 185, fan 162 (on / off).

In the following, various exemplary operating states are described, from which the mode of operation of the prediction system 146 can be seen.

Exemplary operating state 1:

(without deviation)

8th shows a first exemplary operating state of the industrial air supply system 128 without process deviation. The first exemplary operating state thus preferably represents a positive case.

The outside temperature 166 and outside humidity 168 are not constant. The preheat module 154 and the humidifier module 160 are active.

A control system of the industrial air supply system 128 keeps the temperature 170 and the relative humidity 172 of the air conditioned by means of the industrial air supply system 128 at a constant value.

According to the status variables 214 (fan 162 = "on" and valve and pump mode = "automatic") the industrial air supply system 128 is ready for operation.

Due to the constant temperature 170 and relative humidity 172 of the air conditioned by means of the industrial air intake system 128, the industrial air intake system 128 is preferably also ready for production.

Exemplary operating state 2:

(Increase in outside temperature 166 with a decrease in relative outside humidity 168 above the design parameters; influence of a measured disturbance variable 210 on target variable 204)

9 shows a second exemplary operating state of the industrial air supply system 128 with an increase in the outside temperature 166 with a decrease in the relative outside humidity 168 due to a change in the weather.

1. a) Die erhöhte Außentemperatur 166 nach dem Wetterumschwung ist außerhalb der Auslegungsparameter.
2. b) Die Leistung des bisher aktiven Vorheizers-Moduls 154 wird von der Regelung reduziert.
3. c) Da die Reduktion der Heizleistung nicht ausreicht wird von der Regelung das Ventil 183 des Kühlmoduls 156 geöffnet und damit die Kühlleistung erhöht.
4. d) Die Drehfrequenz 193 der Befeuchterpumpe 153 wird von der Regelung erhöht, um die abnehmende Außenfeuchte 168 auszugleichen.
5. e) Da die Kühlleistung aufgrund der zu geringen Auslegung des Kühlmoduls 156 nicht ausreicht, kommt es zu einer Abweichung der Temperatur 170 der mittels der industriellen Zuluftanlage 128 konditionierten Luft.

The process values show the following behavior:

1. a) The increased outside temperature 166 after the weather change is outside of the design parameters.
2. b) The power of the previously active preheater module 154 is reduced by the control.
3. c) Since the reduction in the heating output is not sufficient, the control opens the valve 183 of the cooling module 156 and thus increases the cooling output.
4. d) The rotational frequency 193 of the humidifier pump 153 is increased by the control in order to compensate for the decreasing external humidity 168.
5. e) Since the cooling capacity is insufficient due to the insufficient design of the cooling module 156, the temperature 170 of the air conditioned by means of the industrial supply air system 128 deviates.

Leaving the specified process window for temperature 170 is delayed due to the inertia of the industrial air supply system 128 and the compensation of the regulation.

Exemplary operating state 3:

(Changeover to winter operation with heat recovery; influence of an unmeasured disturbance variable 212 on target variable 204)

10 shows a third exemplary operating state of the industrial air supply system 128 when the heat recovery system 164 is switched on, which heats the air flow 165 from waste heat in cold climatic conditions.

The activation of the heat recovery system 164 takes place via a manual valve, which is why the influence of the heat recovery by that of the heat recovery system 164 cannot be measured (unmeasured disturbance variable 212).

1. a) Die Heizleistung des Wärmerückgewinnungssystems 164 wird erhöht, der Wert ist nicht messbar.
2. b) Das Ventil 181 des Vorheiz-Moduls 154 schließt aufgrund der Erhöhung der Heizleistung.
3. c) Im Kühlmodul 156 öffnet das Ventil 183, um durch zusätzliche Kühlleistung die Temperatur zu halten.
4. d) Die Drehfrequenz 193 der Befeuchterpumpe 153 wird von der Regelung so angepasst, dass die Luftfeuchte 172 der mittels der industriellen Zuluftanlage 128 konditionierten Luft gehalten wird.
5. e) Es kommt zu einer Abweichung der Temperatur 170 der mittels der industriellen Zuluftanlage 128 konditionierten Luft, da das Kühlmodul 156 die Wärmezufuhr nicht schnell genug kompensieren kann. Die Abweichung erfolgt aufgrund der Trägheit der industriellen Zuluftanlage 128 und der Kompensation der Regelung verzögert.

The process values show the following behavior:

1. a) The heating power of the heat recovery system 164 is increased, the value cannot be measured.
2. b) The valve 181 of the preheating module 154 closes due to the increase in the heating power.
3. c) In the cooling module 156, the valve 183 opens in order to maintain the temperature through additional cooling power.
4. d) The rotational frequency 193 of the humidifier pump 153 is adapted by the control so that the air humidity 172 of the air conditioned by means of the industrial air supply system 128 is maintained.
5. e) The temperature 170 of the air conditioned by means of the industrial supply air system 128 deviates, since the cooling module 156 cannot compensate for the supply of heat quickly enough. The deviation is delayed due to the inertia of the industrial air supply system 128 and the compensation of the regulation.

Exemplary operating state 4:

(Failure of valve 181 of preheater module 154)

11 FIG. 4 shows a fourth exemplary operating state of the industrial air supply system 128 with a failure of the valve 181 of the preheating module 154.

1. a) Das Ventil 181 des Vorheiz-Moduls 154 schließt aufgrund eines Gerätefehlers, wodurch sich die Heizleistung verringert.
2. b) Das Ventil 185 des Nachheiz-Moduls 158 öffnet, um die fehlende Heizleistung zu kompensieren.
3. c) Da die Heizleistung des Nachheiz-Moduls 158 nicht ausreicht, kommt es zu einer Abweichung der Temperatur 170 der mittels der industriellen Zuluftanlage 128 konditionierten Luft.

The process values show the following behavior:

1. a) The valve 181 of the preheating module 154 closes due to a device fault, as a result of which the heating output is reduced.
2. b) The valve 185 of the after-heating module 158 opens in order to compensate for the missing heating power.
3. c) Since the heating output of the post-heating module 158 is not sufficient, there is a deviation in the temperature 170 of the air conditioned by means of the industrial supply air system 128.

The method for predicting process deviations in the process engineering system 101, in particular in the industrial air supply system, is explained below with regard to the operating states 1 to 4 described above.

The operating states 2 to 4 with process deviations during the operation of the industrial air intake system 128 can preferably be predicted by means of the method for predicting process deviations with a prediction horizon 216 of, for example, approximately 15 minutes.

The data basis for the training of a prediction model is considered to be a period of time in which the industrial air supply system 128 normally runs (> 80%) in a ready-to-use operating state (cf. exemplary operating state 1).

The recorded data contain the exemplary operating states 2 to 4, preferably several times. These may have occurred during operation or, alternatively, have been brought about intentionally, for example by closing a valve 181, 183, 185.

The data are then preferably preprocessed and regularized, for example 12 can be found. The valve position 180 of the valve 181 of the preheating module 154 and the temperature 170 and relative humidity 172 of the air conditioned by means of the industrial supply air system 128 are plotted there by way of example.

The regularized data are divided into, for example, 30-minute time windows 218, each with a time shift of, for example, 5 minutes.

The data regularized in time window 218 form, in particular, prediction data sets, in particular prediction data sets without process deviations 220 and prediction data sets with process deviation 222 (cf. 7th ).

• - Falls nein: entsprechende Prädiktionsdatensätze mit Prozessabweichung 222 werden verworfen und nicht zum Training des Vorhersagemodells verwendet.
• - Falls ja: entsprechende Prädiktionsdatensätze mit Prozessabweichung 222 kommen für das Training des Vorhersagemodells in Frage.

For the prediction data sets with process deviation 222, a check is made on the basis of the status variables 214 as to whether the process engineering system 101 was ready for operation (for example fan 162 “on”, conditioning module 150 in automatic mode).

• If no: corresponding prediction data sets with process deviation 222 are discarded and not used to train the prediction model.
• If yes: corresponding prediction data sets with process deviation 222 are possible for training the prediction model.

Due to a minimum time interval of, for example, one hour, in 7th only one prediction data set with process deviation 222 is selected.

A selection of the prediction data sets without process deviations 220 is preferably carried out analogously to the selection of the prediction data sets with process deviation 222.

With a minimum time interval of, for example, one hour, only one prediction data set without process deviations 220 is selected for training the prediction model.

Features are then preferably extracted from the selected prediction data sets without process deviations 220 and from the selected prediction data sets with process deviations 222.

Statistical key figures, for example minimum, maximum, median, mean and / or the standard deviation, are used to extract the features. It can also be beneficial if linear regression coefficients are used to extract the features.

The prediction model is preferably trained on the basis of the extracted features of the selected prediction data sets without process deviations 220 and on the basis of the selected prediction data sets with process deviations 222, in particular by means of a machine learning method, for example by means of gradient boosting.

Process deviations from production-critical process values in the industrial air intake system 128 are preferably predicted by means of the trained forecast model on the basis of changing process values during operation of the industrial air intake system 128.

The prediction model is explained in particular using the exemplary operating states 2 to 4:

Exemplary operating state 2:

The prediction model predicts a process deviation after the temperature increase has occurred. The basis for this are the measured disturbance variables 210, in particular the outside temperature 166 and the outside humidity 168, the reaction of the conditioning modules 150 and the course of the temperature 170 of the air conditioned by the industrial air supply system 128 at the discharge part.

Exemplary operating state 3:

After the heat recovery system has been switched on, the prediction model predicts a temperature increase in temperature 170 of the air conditioned by means of the industrial supply air system 128. The basis is the reaction of the conditioning modules 150 as well as the course of the temperature 170 of the air conditioned by means of the industrial supply air system 128 at the discharge part.

Exemplary operating state 4:

The prediction model predicts the temperature increase in the temperature 170 of the air conditioned by means of the industrial supply air system 128 on the basis of the weather conditions and the valve position 180 of the valve 181 of the preheating module 156.

The method for anomaly and / or error detection in the process plant 101 is now preferably described with reference to FIG 14th to 19th explained.

The method for anomaly and / or error detection can preferably be used to identify error situations, in particular defects and / or failures in components, sensors and / or actuators.

The pretreatment station 112 here forms the process engineering system 101, for example.

The pre-treatment station 112 preferably comprises a pre-treatment basin 224 in which workpieces 106, preferably vehicle bodies 108, can be pre-treated.

The pre-treatment station 112 preferably further comprises a first pump 226, a second pump 228, a heat exchanger 230 and a valve 232.

The process values V62Point, S86, T95, T85, T15 and T05 receive their designation based on a unique designation in a numbering system of the process engineering plant 101.

The process values T95, T85, T15 and T05 represent, in particular, temperatures within the process plant 101, in particular within the pretreatment station 112.

The process value S86 is a valve position of the valve 232.

The process value V62Point is a volume flow.

To carry out the method for anomaly and / or error detection, an anomaly and / or error model 233 of the process engineering system 101, in particular of the pretreatment station 112, is preferably created, which includes information about the probability of occurrence of the process values mentioned above (cf. 15th ).

• Zunächst werden Testsignale insbesondere unter Berücksichtigung von Auslegungsdaten 234 im Rahmen einer Testsignalgenerierung 236 erzeugt.
• Insbesondere werden aufgrund der Auslegungsdaten 234 Grenzen für die Testsignale vorgegeben, beispielsweise eine maximale Amplitude von Stellgrößensprüngen bei der Vorgabe von Sprungfunktionen.

The anomaly and / or error model 233 is preferably created as follows:

• First of all, test signals are generated in the context of a test signal generation 236, in particular taking into account design data 234.
• In particular, based on the design data 234, limits are specified for the test signals, for example a maximum amplitude of manipulated variable jumps when specifying step functions.

• - Sensortyp (Temperatursensor, Durchflusssensor, Ventilstellung, Drucksensor etc.) und/oder Aktortyp (Ventil, Ventilator, Klappe, E-Motor);
• - zulässige Wertebereiche von Sensoren und/oder Aktoren;
• - Signaltyp von Sensor und/oder Aktor (float, integer).

The design data 234 include, for example, one or more of the following information:

• - Sensor type (temperature sensor, flow sensor, valve position, pressure sensor etc.) and / or actuator type (valve, fan, flap, electric motor);
• - Permissible value ranges of sensors and / or actuators;
• - Signal type from sensor and / or actuator (float, integer).

The process engineering system 101, in particular the pretreatment station 112, is preferably excited dynamically by means of the test signals. This is in 15th marked with the reference numeral 238. It is conceivable that anomalies and / or error situations are specifically generated during the excitation with test signals.

When the process engineering system 101, in particular the pretreatment station 112, is excited with test signals, system input signals 240 and system output signals 242 are preferably generated.

The system input signals 240 and the system output signals 242 are preferably stored in a test signal database 244.

A structure identification 246 of the process engineering system 101, in particular of the pretreatment station 112, is then preferably carried out. A structure graph 247 of the process engineering system 101, in particular of the pretreatment station 112, is preferably determined (cf. 16 ).

The structure identification 246, in particular the determination of the structure graph, is preferably carried out using a machine learning method, preferably using measures of relationship by means of which non-linear relationships can be reproduced, for example using transinformation (“mutual information”).

It can also be favorable if expert knowledge 248 is used for structure identification 246, that is to say in particular knowledge about relationships in the process.

In this case, for example, edges between nodes of the structure graph to be determined can be excluded by a pre-configuration of the structure graph using information from expert knowledge, known circuit diagrams and / or process schemes 250. In particular, a computational effort for determining the structure graph can be reduced.

It can also be beneficial if the structure graph is determined using the unique designation of the process values based on a numbering system of the process engineering system 101, in particular the pretreatment station 112, i.e. based on semantics 252 of the designation of the process values.

In particular, it is conceivable that the structure graph determined using the machine learning method is checked for plausibility using expert knowledge 248, using known circuit diagrams and / or process schemes 250 and / or using the designations in the numbering system of the process engineering system 101 (semantics 252).

Causalities 254 in the determined process structure, in particular “arrow directions” in the determined structure graph, are then preferably determined.

Causalities 254 in the ascertained process structure are derived, for example, from system input signals 240 and system output signals 242 of the process engineering system 101 ascertained with the excitation of the process engineering system 101 with test signals, for example based on the respective temporal course of the system input signals 240 and the system output signals 242.

As an alternative or in addition to this, it is conceivable that causalities 254 are derived from system input signals 240 and system output signals 242 determined when the process engineering system 101 is excited with test signals using methods for causal inference.

In order to determine the causalities 254, expert knowledge 248, procedural schemes 250 and / or the designations in the numbering system of the procedural plant 101 (semantics 252) are preferably used.

Preferably, by means of the determined causalities 254 in the determined process structure or in the determined structure graph, the process values causing a detected anomaly can be found.

After the structure identification 246 and / or the determination of the causalities 254, a structure parameterization 256 is preferably carried out.

The structure parameterization 256 can preferably be facilitated by means of the structure identification 246. A computational effort for the structure parameterization 256 can preferably be reduced by means of the structure identification 246.

The structure parameterization 256 is preferably carried out using a method for determining probability density functions, in particular using Gaussian mixed models.

The structure parameterization 256 is, for example, for the common probability density function f1 of the in 17th The clique 258 shown was carried out using Gaussian mixed models (cf. 18th ).

Expert knowledge 248 is preferably also used for the structure parameterization 256.

For structure parameterization 256, for example, known physical relationships between process values and / or physical characteristics maps of functional elements of the process engineering system 101, in particular of the pretreatment station, are used. For example, a map of the valve 232 is used.

It can also be beneficial if expert knowledge 248 about error situations is used for structure parameterization 256.

A relationship between the valve position S86 and the volume flow V62Point can be described, for example, by means of a known valve characteristic map of the valve 232.

For structure parameterization 256 using methods for determining probability density functions, in particular using Gaussian mixed models, data from the regular operation of the process engineering system 101, in particular the pretreatment station 112, and / or data from the test signal database 244 are preferably used in an operating database 260.

For example, manipulated, measured and / or controlled variables stored in a database 244, 260 are used for structure parameterization 256 using methods for determining probability density functions.

For structure parameterization 256 using methods for determining probability density functions, data from the ongoing operation of the process plant 101, in particular the pretreatment station, are used, which over a period of at least 2 weeks, preferably at least 4 weeks, for example at least 8 weeks, get saved.

The data are preferably preprocessed before the structure parameterization 256.

During the preprocessing, data from the process plant 101 which are not assigned to operational or production-ready operating states of the process plant 101 (for example shut down plant, maintenance phases, etc.), are preferably excluded, in particular on the basis of alarms and status bits that indicate the state of the process plant 101, in particular the pretreatment station 112.

It can also be favorable if data of the process engineering system 101 are preprocessed by filtering, for example by means of low-pass filters and / or by means of Butterworth filters.

The data are preferably also interpolated to a uniform time step size.

When creating the anomaly and / or error model 233, a limit value for the probability of occurrence of a process value is preferably established as part of a limit value optimization 264.

The limit value for the probability of occurrence is preferably established in such a way that an anomaly is recognized if the limit value is not reached.

The limit value is preferably determined by means of a non-linear optimization method, for example by means of the Nelder-Mead method.

As an alternative or in addition to this, it is conceivable that the limit value is determined using quantiles.

Limit values for the probability of occurrence of the process values can preferably be optimized, for example by specifying a “false positive rate”.

Furthermore, it is conceivable that the limit values are adapted after the anomaly and / or error model 233 has been created for the first time, in particular in the case of an excessive number of false alarms.

• Es kommt beispielsweise zu einem Ventilausfall des Ventils 232 und damit zu einer Abweichung der Sensorwerte vom abgebildeten Normalzustand in den einzelnen Cliquen.

An anomaly and / or error detection using the anomaly and / or error model 233 is preferably carried out as follows:

• For example, there is a valve failure of the valve 232 and thus a deviation of the sensor values from the depicted normal state in the individual cliques.

The probabilities of occurrence of the sensor values in the cliques are evaluated during operation of the process engineering system 101, in particular during operation of the pretreatment station 112, and if the calculated limit values are not reached, detected anomalies occur in the various cliques.

The valve failure of the valve 232 initially leads to an anomaly in the clique 258 of the valve position S86, a message being output by the anomaly and / or error detection system 148.

In the further course of time, further anomalies arise due to the propagation of errors, which later also influence the process-relevant variable, for example the pool temperature T35 of the pool 224.

• - Detektionszeitpunkt der Anomalie;
• - Clique(n) des Auftretens der Anomalie mit beteiligten Sensoren.

The report by the anomaly and / or error detection system 148 preferably contains one or more of the following information:

• - Time of detection of the anomaly;
• - Clique (s) of the occurrence of the anomaly with involved sensors.

Due to the early detection of the anomaly and the notification to the user, a deviation in the process-relevant variable, that is to say the basin temperature T35 of the basin 224, can preferably be prevented if intervention is timely.

The user can then define a cause of failure, i.e. valve failure, for the occurrence of the anomaly.

By assigning the cause of the error, the clique 258 is expanded by a node 266 and the probability density function of the anomalous data is integrated into the functional context (cf. 19th ).

After integrating the cause of the error, the method for anomaly and / or error detection is carried out as before. If an anomaly occurs, the probabilities of the defined error causes are also output.

• - Detektionszeitpunkt der Anomalie;
• - Clique(n) des Auftretens der Anomalie mit beteiligten Sensoren;
• - Wahrscheinlichkeiten der definierten Fehlerursachen.

A user now receives one or more of the following information through the message from the anomaly and / or error detection system 148:

• - time of detection of the anomaly;
• - Clique (s) of the occurrence of the anomaly with involved sensors;
• - Probabilities of the defined causes of errors.

Special embodiments are the following:

Embodiment 1:

• - insbesondere automatisches Erkennen einer Fehlersituation in der verfahrenstechnischen Anlage (101);
• - Speichern eines Fehlersituationsdatensatzes zu der jeweiligen erkannten Fehlersituation in einer Fehlerdatenbank (136);
• - automatische Ermittlung einer Fehlerursache für die Fehlersituation und/oder automatische Ermittlung von für die Fehlersituation relevanten Prozesswerten auf Basis des Fehlerdatensatzes einer jeweiligen erkannten Fehlersituation.

A method for error analysis in a process plant (101), for example in a painting plant (102), the method comprising the following:

• - In particular, automatic detection of an error situation in the process engineering system (101);
• - Storing a fault situation data set for the respective recognized fault situation in a fault database (136);
• - automatic determination of a cause of the error for the error situation and / or automatic determination of process values relevant to the error situation on the basis of the error data record of a respective recognized error situation.

Embodiment 2:

• - einer Vorverknüpfung aus einem Meldesystem;
• - einer Zuordnung eines Prozesswerts zu demselben Anlagenteil der verfahrenstechnischen Anlage (101), in dem die Fehlersituation aufgetreten ist;
• - einer Verknüpfung eines Prozesswerts mit einer historischen Fehlersituation aufgrund einer aktiven Auswahl eines Benutzers;
• - einer aktiven Auswahl des Prozesswerts durch einen Benutzer.

Method according to embodiment 1, characterized in that, in order to automatically determine the cause of the error for the error situation and / or the process values relevant to the error situation, one or more process values are automatically linked to the error situation based on one or more of the following linking criteria:

• - a pre-link from a reporting system;
• - an assignment of a process value to the same part of the process engineering system (101) in which the error situation occurred;
• a link between a process value and a historical error situation based on an active selection by a user;
• - an active selection of the process value by a user.

Embodiment 3:

• - einer Prozessrelevanz der Prozesswerte;
• - einer Position eines Prozesswerts oder eines den Prozesswert ermittelnden Sensors innerhalb der verfahrenstechnischen Anlage (101) ;
• - einem Betrag einer Abweichung eines Prozesswerts von einem definierten Prozessfenster und/oder von einem Normalzustand;
• - einer Priorisierung von historischen Prozesswerten in historischen Fehlersituationen;
• - durch Übernehmen einer Priorisierung der Fehlerursache und/oder der Prozesswerte aus einem Meldesystem (138);
• - einer Priorisierung durch einen Benutzer.

Method according to embodiment 2, characterized in that for the automatic determination of the cause of the error for the error situation and / or the process values relevant for the error situation, an automatic prioritization of the process values linked to the error situation based on one or more of the following prioritization criteria is carried out automatically:

• - a process relevance of the process values;
• - A position of a process value or of a sensor determining the process value within the process engineering system (101);
• an amount of a deviation of a process value from a defined process window and / or from a normal state;
• - a prioritization of historical process values in historical error situations;
• - by taking over a prioritization of the cause of the error and / or the process values from a reporting system (138);
• - a prioritization by a user.

Embodiment 4:

• - einer Prozessrelevanz der Prozesswerte;
• - einer Position eines Prozesswerts oder eines den Prozesswert ermittelnden Sensors innerhalb der verfahrenstechnischen Anlage (101) ;
• - einem Betrag einer Abweichung eines Prozesswerts von einem definierten Prozessfenster und/oder von einem Normalzustand;
• - einer Priorisierung von historischen Prozesswerten in historischen Fehlersituationen;
• - physikalischen Abhängigkeiten der Prozesswerte.

Method according to one of the embodiments 1 to 3, characterized in that further error causes and / or process values are proposed for the automatic determination of the cause of the error for the error situation and / or the process values relevant for the error situation, the proposal being made automatically based on one or more of the following criteria is carried out:

• - a process relevance of the process values;
• - A position of a process value or of a sensor determining the process value within the process engineering system (101);
• an amount of a deviation of a process value from a defined process window and / or from a normal state;
• - a prioritization of historical process values in historical error situations;
• - physical dependencies of the process values.

Embodiment 5:

• - einer Fehlerklassifikation der historischen Fehlersituation;
• - einer historischen Fehlersituation an demselben oder einem vergleichbaren Anlagenteil;
• - identischen oder ähnlichen Prozesswerten der historischen Fehlersituation zu Prozesswerten der erkannten Fehlersituation.

Method according to one of the embodiments 1 to 4, characterized in that historical error situations are determined from an error database (136) using one or more of the following similarity criteria:

• - an error classification of the historical error situation;
• - a historical error situation on the same or a comparable part of the system;
• - identical or similar process values of the historical error situation to process values of the recognized error situation.

Embodiment 6:

Method according to one of the embodiments 1 to 5, characterized in that historical process values are determined from a process database (134) which are identical or similar to process values of the detected error situation.

Embodiment 7:

Method according to embodiment 6, characterized in that the historical process values determined are identified as belonging to a historical error situation.

Embodiment 8:

Method according to one of the embodiments 1 to 7, characterized in that a fault situation data record is stored in a fault database (136) for a recognized fault situation.

Embodiment 9:

• - eine Fehlerklassifikation der Fehlersituation;
• - mit der Fehlersituation verknüpfte Prozesswerte basierend auf einer Vorverknüpfung aus einem Meldesystem;
• - Informationen über einen Zeitpunkt des Auftretens einer jeweiligen Fehlersituation;
• - Informationen über eine Zeitdauer des Auftretens einer jeweiligen Fehlersituation;
• - Informationen über den Ort des Auftretens einer jeweiligen Fehlersituation;
• - Alarme;
• - Statusmeldungen.

Method according to embodiment 8, characterized in that a respective fault identification data record comprises one or more of the following fault situation data:

• - an error classification of the error situation;
• - Process values linked to the error situation based on a pre-link from a reporting system;
• - Information about a point in time when a respective error situation occurred;
• - Information about the duration of the occurrence of a respective error situation;
• - Information about the location of the occurrence of a respective error situation;
• - alarms;
• - Status messages.

Embodiment 10:

Method according to embodiment 8 or 9, characterized in that the error situation data set of a respective error situation comprises error identification data for the clear identification of the detected error situation.

Embodiment 11:

Method according to one of the embodiments 8 to 10, characterized in that documentation data and error correction data are stored in the error situation data record of a respective error situation.

Embodiment 12:

Method according to one of the embodiments 8 to 11, characterized in that process values are stored during operation of the process engineering system (101) in a time-synchronized manner with a recognized error situation.

Embodiment 13:

Method according to one of the embodiments 8 to 12, characterized in that process values are provided with a time stamp, by means of which the process values can be clearly assigned to a point in time.

Embodiment 14:

Error analysis system (144) for error analysis in a process plant (101), for example in a painting plant (102), which is used to carry out the method for error analysis in a process plant (101), for example in a painting plant (102), according to one of the embodiments 1 to 13 is formed and set up.

Embodiment 15:

An industrial control system (100) comprising a failure analysis system (144) according to embodiment 14.

Embodiment 16:

• - automatisches Erstellen eines Vorhersagemodells;
• - Vorhersage von Prozessabweichungen im Betrieb der verfahrenstechnischen Anlage (101) unter Verwendung des Vorhersagemodells.

A method for predicting process deviations in a process plant (101), for example in a painting plant (102), the method comprising the following:

• - automatic creation of a predictive model;
• - Prediction of process deviations in the operation of the process plant (101) using the prediction model.

Embodiment 17:

Method according to embodiment 16, characterized in that the method for predicting process deviations in an industrial air supply system (128), in a pretreatment station (112), in a station for cathodic dip painting (114) and / or in a dryer station (116, 120, 124) is carried out.

Embodiment 18:

Method according to embodiment 16 or 17, characterized in that process deviations from production-critical process values in the process engineering system (101) are predicted by means of the forecast model on the basis of changing process values during operation of the process engineering system (101).

Embodiment 19:

Method according to one of the embodiments 16 to 18, characterized in that process values and / or status variables are stored over a predetermined period of time during operation of the process engineering system (101) for the automatic creation of the prediction model.

Embodiment 20:

• - die verfahrenstechnische Anlage (101) ist in dem vorgegebenen Zeitraum zu mindestens ungefähr 60 %, vorzugsweise zu mindestens ungefähr 80 %, in einem betriebsbereiten Zustand, insbesondere für einen Produktionsbetrieb;
• - die verfahrenstechnische Anlage (101) ist in dem vorgegebenen Zeitraum zu mindestens ungefähr 60 %, vorzugsweise zu mindestens ungefähr 80 %, in einem produktionsfähigen Zustand;
• - die verfahrenstechnische Anlage (101) wird in dem vorgegebenen Zeitraum insbesondere mit sämtlichen möglichen Betriebsstrategien betrieben;
• - einer vorgegebene Anzahl von Prozessabweichungen und/oder Störfällen in dem vorgegebenen Zeitraum.

Method according to embodiment 19, characterized in that the predefined period of time over which process values and / or status variables are stored in the operation of the process plant (101) is predefined as a function of one or more of the following criteria:

• - The process-engineering system (101) is in an operational state for at least approximately 60%, preferably at least approximately 80%, in the specified period, in particular for a production operation;
• - The process engineering plant (101) is in a productive state for at least approximately 60%, preferably at least approximately 80%, in the specified period;
• - The procedural plant (101) is operated in particular with all possible operating strategies in the specified period;
• - a specified number of process deviations and / or incidents in the specified period.

Embodiment 21:

Method according to embodiment 19 or 20, characterized in that a machine learning method is carried out to create the prediction model, the process values and / or status variables stored over the predetermined period of time being used to create the prediction model.

Embodiment 22:

Method according to embodiment 21, characterized in that the machine learning method is carried out on the basis of features which are extracted from the process values and / or status variables stored over the predetermined period of time.

Embodiment 23:

• - statistische Kennzahlen;
• - Koeffizienten aus einer Hauptkomponentenanalyse;
• - lineare Regressionskoeffizienten;
• - dominante Frequenzen und/oder Amplituden aus dem Fourier-Spektrum.

Method according to embodiment 22, characterized in that one or more of the following are used to extract the features:

• - statistical indicators;
• - coefficients from a principal component analysis;
• - linear regression coefficients;
• - dominant frequencies and / or amplitudes from the Fourier spectrum.

Embodiment 24:

Method according to one of the embodiment 16 to 23, characterized in that a selected number of prediction data sets with process deviations ( 222) and a selected number of Prediction data sets without process deviations ( 220 ) be used.

Embodiment 25:

• - einem zeitlichen Mindestabstand zwischen zwei Prädiktionsdatensätzen mit Prozessabweichungen;
• - einer automatischen Auswahl anhand von definierten Regeln;
• - einer Auswahl durch einen Benutzer.

Method according to embodiment 24, characterized in that the selection of the number of prediction data sets with process deviation is based on one or more of the following criteria:

• - a minimum time interval between two prediction data sets with process deviations;
• - an automatic selection based on defined rules;
• - a selection by a user.

Embodiment 26:

Method according to embodiment 24 or 25, characterized in that prediction data sets with process deviations are identified as such if a process deviation occurs within a predetermined time interval.

Embodiment 27:

Method according to embodiment 26, characterized in that the process values and / or status variables stored over the predetermined period of time are combined into prediction data sets by preprocessing.

Embodiment 28:

• - Regularisierung der über den vorgegebenen Zeitraum gespeicherten Prozesswerte;
• - Zusammenfassung der Prozesswerte und/oder Statusgrößen in Prädiktionsdatensätze durch Einteilung der Prozesswerte und/oder Statusgrößen in Zeitfenster mit zeitlicher Verschiebung.

Method according to embodiment 27, characterized in that the preprocessing comprises:

• - Regularization of the process values stored over the specified period;
• - Summary of the process values and / or status variables in prediction data sets by dividing the process values and / or status variables into time windows with a time shift.

Embodiment 29:

Forecast system ( 146 ) to predict process deviations in a process engineering plant, which is used to carry out the method for predicting process deviations in a process engineering plant ( 101 ), for example in a paint shop ( 102 ), is designed and set up according to one of the embodiments 16 to 29.

Embodiment 30:

Industrial control system ( 100 ), which is a forecast system ( 146 ) according to embodiment 29.

Embodiment 31:

• - automatisches Erstellen eines Anomalie- und/oder Fehlermodells (233) der verfahrenstechnischen Anlage (101), welches Informationen über die Auftretenswahrscheinlichkeit von Prozesswerten umfasst;
• - automatisches Einlesen von Prozesswerten der verfahrenstechnischen Anlage (101) im Betrieb derselben;
• - automatische Erkennung einer Anomalie und/oder einer Fehlersituation durch Ermitteln einer Auftretenswahrscheinlichkeit mittels des Anomalie- und/oder Fehlermodells (233) auf Basis der eingelesenen Prozesswerte der verfahrenstechnischen Anlage (101) und durch Überprüfen der Auftretenswahrscheinlichkeit auf einen Grenzwert.

Procedure for anomaly and / or error detection in a process plant ( 101 ), for example in a paint shop ( 102 ), the method comprising:

• - automatic creation of an anomaly and / or failure model ( 233 ) of the process plant ( 101 ), which includes information about the probability of occurrence of process values;
• - automatic reading of process values from the process plant ( 101 ) in the operation of the same;
• - Automatic detection of an anomaly and / or an error situation by determining a probability of occurrence using the anomaly and / or error model ( 233 ) on the basis of the process values read in from the process plant ( 101 ) and by checking the probability of occurrence for a limit value.

Embodiment 32:

• - das Anomalie- und/oder Fehlermodell (233) Strukturdaten umfasst, welche Informationen über eine Prozessstruktur in der verfahrenstechnischen Anlage (101) enthalten, und/oder dass
• - das Anomalie- und/oder Fehlermodell (233) Parametrierungsdaten umfasst, welche Informationen über Zusammenhänge zwischen Prozesswerten der verfahrenstechnischen Anlage (101) enthalten.

Method according to embodiment 31, characterized in that

• - the anomaly and / or failure model ( 233 ) Includes structural data, which contains information about a process structure in the process plant ( 101 ) included, and / or that
• - the anomaly and / or failure model ( 233 ) Parameterization data includes which information about relationships between process values of the process engineering plant ( 101 ) contain.

Embodiment 33:

• - Strukturidentifikation (246) zur Ermittlung einer Prozessstruktur der verfahrenstechnischen Anlage (101);
• - Ermittlung von Kausalitäten (254) in der ermittelten Prozessstruktur der verfahrenstechnischen Anlage (101);
• - Strukturparametrierung (256) der Zusammenhänge in der ermittelten Prozessstruktur der verfahrenstechnischen Anlage (101).

Method according to embodiment 31 or 32, characterized in that to create the anomaly and / or error model ( 233 ) one or more of the following steps must be performed:

• - Structure identification ( 246 ) to determine a process structure of the process plant ( 101 );
• - Determination of causalities ( 254 ) in the determined process structure of the process plant ( 101 );
• - Structure parameterization ( 256 ) the relationships in the determined process structure of the process plant ( 101 ).

Embodiment 34:

Method according to embodiment 33, characterized in that in the structure identification ( 246 ) to determine a process structure of the process plant ( 101 ) a structure graph is determined, which in particular shows relationships in the process engineering system ( 101 ).

Embodiment 35:

• - eines maschinellen Lernverfahrens;
• - Expertenwissen (248);
• - bekannte Schaltplänen und/oder Verfahrensschemata (250);
• - Bezeichnungen in einem Nummerierungssystem der verfahrenstechnischen Anlage (101).

Method according to embodiment 34, characterized in that the structure graph is determined using one or more of the following:

• - a machine learning process;
• - Expert knowledge ( 248 );
• - known circuit diagrams and / or process schemes ( 250 );
• - Designations in a numbering system of the process plant ( 101 ).

Embodiment 36:

Method according to embodiment 33 to 35, characterized in that the procedural plant ( 101 ) for structure identification, in particular for determining the structure graph, is stimulated with test signals.

Embodiment 37:

• - bei der Anregung der verfahrenstechnischen Anlage (101) mit Testsignalen generierte Systemeingangssignale (240) und Systemausgangssignale (242);
• - Expertenwissen (248);
• - bekannten Schaltplänen und/oder Verfahrensschemata (252);
• - Bezeichnungen in einem Nummerierungssystem der verfahrenstechnischen Anlage (101).

Method according to one of the embodiments 33 to 36, characterized in that the determination of causalities ( 254 ) in the determined process structure of the process plant ( 101 ) is made using one or more of the following:

• - when stimulating the process plant ( 101 ) system input signals generated with test signals ( 240 ) and system output signals ( 242 );
• - Expert knowledge ( 248 );
• - known circuit diagrams and / or process diagrams ( 252 );
• - Designations in a numbering system of the process plant ( 101 ).

Embodiment 38 :

• - Verfahren zur Ermittlung von Wahrscheinlichkeitsdichtefunktionen, insbesondere Gaußsche Mischmodelle;
• - bekannte physikalische Zusammenhänge zwischen Prozesswerten;
• - physikalische Kennfelder von Funktionselementen der verfahrenstechnischen Anlage (101), beispielsweise Kennfelder von Ventilen (232).

Method according to one of the embodiments 33 to 37, characterized in that for the structure parameterization ( 246 ) the relationships in the determined process structure of the process plant ( 101 ) one or more of the following may be used:

• - Method for determining probability density functions, in particular Gaussian mixed models;
• - known physical relationships between process values;
• - physical maps of functional elements of the process plant ( 101 ), e.g. valve maps ( 232 ).

Embodiment 39:

Method according to embodiment 38, characterized in that for structural parameterization ( 246 ) using methods for determining probability density functions, in particular using Gaussian mixed models, data from the regular operation of the process engineering plant ( 101 ) and / or by stimulating the process plant ( 101 ) data obtained by means of test signals are used.

Embodiment 40:

Method according to embodiment 39, characterized in that the structure parameterization ( 246 ) using methods for determining probability density functions, in particular using Gaussian mixed models, data used before the structure parameterization ( 246 ) are preprocessed.

Embodiment 41:

Method according to one of the embodiments 31 to 40, characterized in that when creating the anomaly and / or error model ( 233 ) a limit value for the probability of occurrence of a process value is established, an anomaly being detected if the limit value is not reached.

Embodiment 42:

Method according to one of the embodiments 31 to 41, characterized in that the method for anomaly and / or error detection is used to identify an error cause of a recognized anomaly and / or a recognized error situation.

Embodiment 43:

• - Vorbehandlungsstation (112);
• - Station zur kathodischen Tauchlackierung (114);
• - Trocknerstationen (116, 120, 124);
• - eine industrielle Zuluftanlage (128);
• - Lackierroboter.

Method according to one of the embodiments 31 to 42, characterized in that the process engineering plant ( 101 ) one or more of the following treatment stations ( 104 ) includes or is formed by a paint shop:

• - pre-treatment station ( 112 );
• - Station for cathodic dip painting ( 114 );
• - dryer stations ( 116 , 120 , 124 );
• - an industrial air supply system ( 128 );
• - painting robots.

Embodiment 44:

Anomaly and / or error detection system ( 148 ) for anomaly and / or error detection, which is used to carry out the method for anomaly and / or error detection in a process plant ( 101 ), for example in a paint shop ( 102 ), is designed and set up according to one of the embodiments 31 to 43.

Embodiment 45:

Industrial control system ( 100 ), which has an anomaly and / or error detection system ( 148 ) according to embodiment 44 comprises.

Embodiment 46:

Industrial control system, which comprises a fault analysis system according to embodiment 14, a prediction system for predicting process deviations in a process engineering plant according to embodiment 29 and / or an anomaly and / or error detection system according to embodiment 44.

Claims (15)

A method for predicting process deviations in a process plant (101), for example in a painting plant (102), the method comprising the following: - automatic creation of a predictive model; - Prediction of process deviations in the operation of the process plant (101) using the prediction model. Procedure according to Claim 1 , characterized in that the method for predicting process deviations is carried out in an industrial air supply system (128), in a pretreatment station (112), in a station for cathodic dip painting (114) and / or in a dryer station (116, 120, 124) . Procedure according to Claim 1 or 2 , characterized in that process deviations from production-critical process values in the process plant (101) are predicted by means of the forecast model on the basis of changing process values during operation of the process plant (101). Method according to one of the Claims 1 to 3 , characterized in that, for the automatic creation of the prediction model, process values and / or status variables are stored during the operation of the process engineering system (101) over a predetermined period of time. Procedure according to Claim 4 , characterized in that the predetermined period of time over which process values and / or status variables are stored in the operation of the procedural plant (101) is predetermined depending on one or more of the following criteria: - the procedural plant (101) is in the predetermined period at least approximately 60%, preferably at least approximately 80%, in an operational state, in particular for a production plant; - The process engineering plant (101) is in a productive state for at least approximately 60%, preferably at least approximately 80%, in the specified period; - The procedural plant (101) is operated in particular with all possible operating strategies in the specified period; - a specified number of process deviations and / or incidents in the specified period. Procedure according to Claim 4 or 5 , characterized in that a machine learning method is carried out to create the prediction model, the process values and / or status variables stored over the predetermined period of time being used to create the prediction model. Procedure according to Claim 6 , characterized in that the machine learning method is carried out on the basis of features which are extracted from the process values and / or status variables stored over the specified period. Procedure according to Claim 7 , characterized in that one or more of the following are used to extract the features: - statistical key figures; - coefficients from a principal component analysis; - linear regression coefficients; - dominant frequencies and / or amplitudes from the Fourier spectrum. Method according to one of the Claims 1 to 8th , characterized in that a selected number of prediction data sets with process deviations (222) and a selected number of prediction data sets without process deviations (220) are used for training the prediction model. Procedure according to Claim 9 , characterized in that the number of prediction data sets with process deviations is selected on the basis of one or more of the following criteria: a minimum time interval between two prediction data sets with process deviations; - an automatic selection based on defined rules; - a selection by a user. Procedure according to Claim 9 or 10 , characterized in that prediction data sets with process deviations are identified as such if a process deviation occurs within a predetermined time interval. Procedure according to Claim 11 , characterized in that the process values and / or status variables stored over the predetermined period are summarized in prediction data sets by preprocessing. Procedure according to Claim 12 , characterized in that the preprocessing comprises the following: regularization of the process values stored over the predetermined period of time; - Summary of the process values and / or status variables in prediction data sets by dividing the process values and / or status variables into time windows with a time shift. Prediction system (146) for predicting process deviations in a process engineering plant, which is used to carry out the method for predicting process deviations in a process engineering plant (101), for example in a painting plant (102), according to one of the Claims 1 to 13 is trained and established. Industrial control system (100) comprising a prediction system (146) according to Claim 14 includes.

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