EP1451689A2 - Method and system for processing fault hypotheses - Google Patents

Method and system for processing fault hypotheses

Info

Publication number
EP1451689A2
EP1451689A2 EP02777189A EP02777189A EP1451689A2 EP 1451689 A2 EP1451689 A2 EP 1451689A2 EP 02777189 A EP02777189 A EP 02777189A EP 02777189 A EP02777189 A EP 02777189A EP 1451689 A2 EP1451689 A2 EP 1451689A2
Authority
EP
European Patent Office
Prior art keywords
error
hypothesis
checklist
processing
fault
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP02777189A
Other languages
German (de)
French (fr)
Inventor
Gerhard Vollmar
Zaijun Hu
Pousga Kabore
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Intellectual Ventures II LLC
Original Assignee
ABB Research Ltd Switzerland
ABB Research Ltd Sweden
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by ABB Research Ltd Switzerland, ABB Research Ltd Sweden filed Critical ABB Research Ltd Switzerland
Publication of EP1451689A2 publication Critical patent/EP1451689A2/en
Withdrawn legal-status Critical Current

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Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0218—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults
    • G05B23/0243—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model
    • G05B23/0245—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterised by the fault detection method dealing with either existing or incipient faults model based detection method, e.g. first-principles knowledge model based on a qualitative model, e.g. rule based; if-then decisions
    • G05B23/0248—Causal models, e.g. fault tree; digraphs; qualitative physics
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
    • G06F11/2257—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using expert systems

Definitions

  • the invention relates to a method and a system for determining the causes of errors and for carrying out their verification in the context of a model-based analysis of the causes of errors.
  • the invention is suitable for supporting an error cause analysis in the event of an error event in a technical system or in a technical process carried out with it.
  • Model-based root cause analysis is described, for example, in G. Vollmar, R. Milanovic, J. Kallela: Model-based Root Cause Analysis, Conference proceedings, 2001 Machinery Reliability Conference, April 2-4, Phoenix Arizona, published by RELIABILITY Magazine, c / o Industrial Communications, Inc. 1704 Natalie Nehs, Dr. Knoxvvette, TN 37931 United States.
  • the method provides an error analyst with information in such a way that he can find the cause of the error quickly and in a targeted manner.
  • the fault analyst needs a computer that is equipped with a web browser and can access the fault cause analysis models via an internet connection.
  • a fault model is a hierarchical, tree-like structure. The top level consists of the error event.
  • the levels below consist of nodes that each represent hypotheses. These knots are linked together like a tree. Each node has a checklist that can be used to verify or negate hypotheses.
  • a checklist consists of several checklist items. These checklist items provide instructions on what information the analyst needs and how he must process it to verify the hypothesis.
  • searching for a malfunction in a system the fault analyst navigates from node to node and checks his system using the attached checklists. If he accepts a hypothesis in this way, he navigates to the underlying error model or the error that led to the malfunction of his system. Processing the checklist items for verification of error hypotheses can, however, be very complex. All meaningful data must be collected and processed.
  • Control systems and databases that store signals with a time reference generally have data that can be used to verify hypotheses. There are also software programs that can compress and process this data into higher-quality information.
  • a significant remaining disadvantage of the known procedure for error cause analysis is that the information from control systems and their databases is not automatically made available to the systems for error cause analysis, nor is a computer-supported verification of hypotheses possible.
  • the invention is therefore based on the object of specifying a method and a system for automated processing of a predefined error hypothesis.
  • the invention accordingly relates to a method and a system for ascertaining the causes of errors and for carrying out their verification as part of an analysis of the causes of errors, including computer-aided processing of checklist items on the basis of physical models for the verification of hypotheses.
  • the method and the system are suitable for supporting the search for causes of faults in the event of fault events occurring in industrial plants.
  • Online data from industrial information technology for example from a control system or planning system, is converted in real-time into higher-value information for the cause of the fault using physical models.
  • the physical models thus provide the information necessary to process checklist items.
  • all checklist points can be processed automatically using physical models, thus verifying a predefined error hypothesis.
  • the results obtained are expediently made available to a system for analyzing the causes of errors via an XML interface.
  • an error analyst is signaled the hypotheses and checklist items that have already been processed by the models.
  • the system includes an input / output device 10, a hypothesis processing device 20 and a data memory 30.
  • the input / output device 10 includes a Model browser 11, with a fault, based fault tree leranalytiker with RCA (root cause analysis) models bezeichne- can edit knowledge-based models 33.
  • a fault, based fault tree leranalytiker with RCA (root cause analysis) models bezeichne- can edit knowledge-based models 33.
  • an error hypothesis can thus be specified, the verification of which can be carried out automatically by means of the system.
  • the hypothesis processing device 20 contains a processing device 21 designated as a model engine for physical models 31 and a hypothesis processor 22 designated as an RCA model navigator in FIG. 1.
  • the processing device 21 accesses process data cyclically, which a data server 40 provides, performs a calculation of System and process states according to the specification of a physical model 31, and stores the result in a data storage area for calculation results 32.
  • the hypothesis processor 22 accesses these calculation results 32 as well as checklists of the knowledge-based models 33 when processing a hypothesis.
  • the data memory 30 contains memory areas with files in which the physical models 31 and knowledge-based models 33 are stored and in which the calculation results 32 are stored.
  • FIG. 2 shows the method for the automatic processing of error hypotheses with the aid of the physical models shown in FIG. 1 in general and in FIG. 3 by way of example.
  • the fault analyst first navigates to a fault hypothesis in order to start the method.
  • the hypothesis processor 22 loads the calculation results 32 required for verifying the hypothesis.
  • the hypothesis processor 22 also loads the checklist of the relevant hypothesis from the knowledge-based models 33.
  • FIG. 6 shows an example of such a checklist.
  • the hypothesis processor 22 compares the calculation results with the checklist items on the checklist.
  • the checklist items for which models are stored are automatically evaluated.
  • Each checklist item contains a condition for verifying the hypothesis.
  • the hypothesis processor 22 identifies whether the checklist item meets or does not meet the condition.
  • FIG. 7 shows an example of how a checklist is output after processing.
  • 3 shows an example of the physical model of a chemical process in a reactor.
  • the model is given in the form of a differential equation.
  • the model describes the process parameters in an error-free state.
  • an error can be determined by comparing the calculated parameter with the real measured value. For example, the inlet and outlet temperatures of the cooling water can be calculated. If the calculated outlet temperature deviates from the measured value, an appropriate system of equations can be used to deduce a measurement value error taking certain boundary conditions into account.
  • the temperature measurement error can be diagnosed with T 0 and a leak with V, for example.
  • FIG. 4 shows the basic representation of an error model as a knowledge-based model 33.
  • the top level shown contains a process model with its process steps. Each process step can be subdivided into further process steps. There are error events and critical components for each process step. In addition there are error trees with nodes. The nodes of a fault tree represent fault hypotheses. A checklist for verification is an essential part of the content of an error hypothesis. The contents of a hypothesis are discussed in more detail in FIG. 5.
  • the model has a hierarchical structure and contains two levels in its simplest form.
  • the top level represents the error event.
  • Several fault hypotheses can be subordinate to one fault event.
  • the logical dependency can be formulated as follows: One or more error hypotheses can be the cause of the error event.
  • Error event and error hypothesis have a similar content description.
  • the error hypothesis can refer to other error models for in-depth analysis, i.e. a fault tree can consist of several subtrees.
  • the connection is established using the fault tree reference attribute.
  • FIG. 6 shows an example of how the system presents the error hypothesis "energy supply too high” to a user.
  • a description of the error hypothesis is explained the connection between the error and the possible cause.
  • a localization indicates the possible fault location; in the example this is the reactor XY.
  • the hypothesis is verified by working through a verification checklist. The tests "Temperature measurement error” and “Leakage to the cooling jacket” can be automatically verified by a physical model.
  • An error tree reference enables access to an associated error tree for the diagnostic criterion "Incorrect operating instructions" for deeper diagnosis.
  • Diagnostic criteria that have already been automatically negatively verified are shown in italics.
  • a positively verified diagnostic criterion is shown in bold and highlighted with an exclamation mark.
  • Diagnostic criteria to be checked are shown in bold and with question marks.

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  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • General Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Computer Hardware Design (AREA)
  • Quality & Reliability (AREA)
  • Test And Diagnosis Of Digital Computers (AREA)
  • Testing And Monitoring For Control Systems (AREA)
  • Debugging And Monitoring (AREA)

Abstract

The invention relates to a method and to a system suited for carrying out the same for conducting the automated processing of fault hypotheses within the framework of an analysis of the cause of a fault in the event thereof in a technical installation. According to the invention, a data processing system (1) is used that contains: physical models (31) of installation functions and processes, which can be carried out by the installation, and knowledge-based models (33) for performing the analysis of the cause of a fault; means (21, 32) for calculating and storing installation and process states while accessing the physical models (31) and data, said data being are stored in a data server (40) of the technical installation; means for performing hypothesis processing (22), and; an input/output device (11).

Description

Verfahren und System zur Bearbeitung von Fehlerhvpothesen Method and system for processing error prostheses
Beschreibungdescription
Die Erfindung betrifft ein Verfahren und ein System zur Ermittlung von Fehlerursachen und zur Durchführung ihrer Verifikation im Rahmen einer Modell-basierten Fehlerursachenanalyse. Die Erfindung ist geeignet zur Unterstützung einer Fehlerursachenanalyse im Fall eines Fehlerereignisses in einer technischen Anlage bzw. in einem damit durchgeführten technischen Prozeß.The invention relates to a method and a system for determining the causes of errors and for carrying out their verification in the context of a model-based analysis of the causes of errors. The invention is suitable for supporting an error cause analysis in the event of an error event in a technical system or in a technical process carried out with it.
Die Modell-basierte Fehlerursachenanalyse ist beispielsweise beschrieben in G. Vollmar, R. Milanovic, J. Kallela: Model-based Root Cause Analysis, Conference proceedings, 2001 Machinery Reliability Conference, April 2-4, Phoenix Arizona, published by RELIABILITY Magazine, c/o Industrial Communications, Inc. 1704 Natalie Nehs, Dr. Knoxviile, TN 37931 USA. Das Verfahren stellt im Fall eines eintretenden Fehlerereignisses einem Fehleranalysten Informationen in solcher Weise bereit, daß er schnell und zielgerichtet die Fehlerursache finden kann. Der Fehleranalyst benötigt dazu einen Computer, der mit einem Web Browser ausgestattet ist und über eine Internetverbindung auf die Fehlerursachenanalysemodelle zugreifen kann. Ein Fehlermodell ist eine hierarchische, baumartige Struktur. Die oberste Ebene besteht aus dem Fehlerereignis. Die Ebenen darunter bestehen aus Knoten welche jeweils Hypothesen darstellen. Diese Knoten sind baumartig miteinander verkettet. Jeder Knoten verfügt über eine Checkliste, an Hand derer sich Hypothesen verifizieren oder negieren lassen. Eine Checkliste setzt sich aus mehreren Checklistenpunkten zusammen. Diese Checklistenpunkte geben Anweisungen welche Informationen der Analyst braucht und wie er sie verarbeiten muß, um die Hypothese zu verifizieren. Bei der Suche nach einer Betriebsstörung in einer Anlage navigiert der Fehleranalyst von Knoten zu Knoten und überprüft seine Anlage an Hand der angehängten Checklisten. Wenn er eine Hypothese auf diese Art akzeptiert, navigiert er zum unterliegenden Fehlermodell bzw. zum Fehler der zur Störung seiner Anlage geführt hat. Das Abarbeiten der Checklistenpunkte zur Verifikation von Fehlerhypothesen kann allerdings sehr aufwendig sein. Sämtliche aussagekräftigen Daten müssen gesammelt und verarbeitet werden. Oftmals sind die Daten, die man zur Verarbeitung heranziehen müßte, nicht mehr vorhanden oder nur schwierig und zeitaufwendig zu beschaffen. Nicht selten müssen die Daten mit komplexen mathematischen Funktionen zu aussagekräftigen Informationen weiterverarbeitet werden. Probleme entstehen insbesondere dann, wenn der Zeitaufwand dafür sehr hoch ist, oder wenn kein Fachmann für diese Aufgabe zur Verfügung steht.Model-based root cause analysis is described, for example, in G. Vollmar, R. Milanovic, J. Kallela: Model-based Root Cause Analysis, Conference proceedings, 2001 Machinery Reliability Conference, April 2-4, Phoenix Arizona, published by RELIABILITY Magazine, c / o Industrial Communications, Inc. 1704 Natalie Nehs, Dr. Knoxviile, TN 37931 United States. In the event of an error event, the method provides an error analyst with information in such a way that he can find the cause of the error quickly and in a targeted manner. The fault analyst needs a computer that is equipped with a web browser and can access the fault cause analysis models via an internet connection. A fault model is a hierarchical, tree-like structure. The top level consists of the error event. The levels below consist of nodes that each represent hypotheses. These knots are linked together like a tree. Each node has a checklist that can be used to verify or negate hypotheses. A checklist consists of several checklist items. These checklist items provide instructions on what information the analyst needs and how he must process it to verify the hypothesis. When searching for a malfunction in a system, the fault analyst navigates from node to node and checks his system using the attached checklists. If he accepts a hypothesis in this way, he navigates to the underlying error model or the error that led to the malfunction of his system. Processing the checklist items for verification of error hypotheses can, however, be very complex. All meaningful data must be collected and processed. The data that would have to be used for processing are often no longer available or are difficult and time-consuming to obtain. It is not uncommon for the data to be processed into meaningful information using complex mathematical functions. Problems arise in particular when the time required for this is very high or when no specialist is available for this task.
Leitsysteme und Datenbanken, die Signale mit Zeitbezug speichern, verfügen prinzipiell über Daten, die zur Verifikation von Hypothesen herangezogen werden können. Auch gibt es Softwareprogramme, die diese Daten zu höherwertigen Informationen verdichten und verarbeiten können. Ein signifikanter verbleibender Nachteil der bekannten Vorgehensweise bei der Fehlerursachenanalyse besteht aber darin, daß die Information von Leitsystemen und deren Datenbanken den Systemen für die Fehlerursachenanalyse nicht automatisch zugänglich gemacht wird, und auch nicht rechnerunterstützt eine Verifikation von Hypothesen ermöglicht wird.Control systems and databases that store signals with a time reference generally have data that can be used to verify hypotheses. There are also software programs that can compress and process this data into higher-quality information. A significant remaining disadvantage of the known procedure for error cause analysis is that the information from control systems and their databases is not automatically made available to the systems for error cause analysis, nor is a computer-supported verification of hypotheses possible.
Der Erfindung liegt daher die Aufgabe zugrunde, ein Verfahren und ein System zur automatisierten Bearbeitung einer vorgegeben Fehlerhypothese anzugeben.The invention is therefore based on the object of specifying a method and a system for automated processing of a predefined error hypothesis.
Diese Aufgabe wird durch ein Verfahren zur Bearbeitung von Fehlerhypothesen im Rahmen einer Fehlerursachenanalyse gelöst, das die im Anspruch 1 angegebenen Merkmale aufweist. Ein entsprechendes System und vorteilhafte Ausgestaltungen sind in weiteren Ansprüchen angegeben.This object is achieved by a method for processing error hypotheses as part of an error cause analysis, which has the features specified in claim 1. A corresponding system and advantageous refinements are specified in further claims.
Die Erfindung bezieht sich demnach auf ein Verfahren und ein System zur Ermittlung von Fehlerursachen und zur Durchführung ihrer Verifikation im Rahmen einer Fehlerursachenanalyse einschließlich einer rechnerunterstützten Bearbeitung von Checklistenpunkten auf Basis physikalischer Modelle zur Verifikation von Hypothesen. Das Verfahren und das System sind geeignet zur Unterstützung der Fehlerursachensuche im Fall von eintretenden Fehlerereignissen in industriellen Anlagen. Online-Daten der industriellen Informationstechnologie, also z.B. aus einem Leitsystem oder Planungssystem werden dabei mit Hilfe physikalischer Modelle in Echtzeit in höherwertige Information für die Fehlerursachenanalyse überführt. Die physikalischen Modelle stellen somit die Information bereit, die zum Abarbeiten von Checklistenpunkten notwendig ist. Im Idealfall können sämtliche Checkiistenpunkte durch physikalische Modelle automatisch abgearbeitet werden und somit eine vorgegebene Fehlerhypothese verifiziert werden. Damit erzielte Ergebnisse werden zweckmäßig über eine XML-Schnittstelle einem System für die Fehlerursachenanalyse bereitgestellt. Einem Fehleranalysten werden beim Abarbeiten eines Fehlerbaumes die Hypothesen und Checklistenpunkte signalisiert, die von den Modellen bereits abgearbeitet wurden.The invention accordingly relates to a method and a system for ascertaining the causes of errors and for carrying out their verification as part of an analysis of the causes of errors, including computer-aided processing of checklist items on the basis of physical models for the verification of hypotheses. The method and the system are suitable for supporting the search for causes of faults in the event of fault events occurring in industrial plants. Online data from industrial information technology, for example from a control system or planning system, is converted in real-time into higher-value information for the cause of the fault using physical models. The physical models thus provide the information necessary to process checklist items. Ideally, all checklist points can be processed automatically using physical models, thus verifying a predefined error hypothesis. The results obtained are expediently made available to a system for analyzing the causes of errors via an XML interface. When processing a fault tree, an error analyst is signaled the hypotheses and checklist items that have already been processed by the models.
Eine weitere Beschreibung des Verfahrens und eines zur Durchführung geeigneten Systems erfolgt nachstehend anhand eines in Zeichnungsfiguren dargestellten Ausführungsbeispiels.A further description of the method and of a system suitable for implementation is given below using an exemplary embodiment shown in the drawing figures.
Es zeigt:It shows:
Fig. 1 ein System zur automatischen Bearbeitung einer Fehlerhypothese,1 shows a system for the automatic processing of an error hypothesis,
Fig. 2 ein Verfahren zur automatischen Bearbeitung von Fehlerhypothesen,2 shows a method for the automatic processing of error hypotheses,
Fig. 3 das physikalische Modell eines Prozesses,3 shows the physical model of a process,
Fig. 4 die prinzipielle Darstellung eines Fehlermodells,4 shows the basic representation of an error model,
Fig. 5 die Struktur eines Fehlerbaums,5 shows the structure of a fault tree,
Fig. 6 eine Fehlerhypothese "Energiezufuhr zu hoch", und6 shows an error hypothesis "energy supply too high", and
Fig. 7 eine automatisch verifizierte Checkliste.7 shows an automatically verified checklist.
Fig. 1 zeigt ein System 1 zur automatischen Bearbeitung von Fehlerhypothesen mit Hilfe physikalischer Modelle. Das System enthält eine Ein/Ausgabe Einrichtung 10, eine Hypothesenverarbeitungseinrichtung 20 und einen Datenspeicher 30.1 shows a system 1 for the automatic processing of error hypotheses with the aid of physical models. The system includes an input / output device 10, a hypothesis processing device 20 and a data memory 30.
Die Ein/Ausgabe Einrichtung 10 enthält einen Modell-Browser 11 , mit dem ein Feh-, leranalytiker Fehlerbaum-basierte, mit RCA(root cause analysis)-Modelle bezeichne- te wissensbasierte Modelle 33 bearbeiten kann. Es kann insbesondere damit eine Fehlerhypothese vorgegeben werden, deren Verifikation mittels des Systems automatisiert durchführbar ist.The input / output device 10 includes a Model browser 11, with a fault, based fault tree leranalytiker with RCA (root cause analysis) models bezeichne- can edit knowledge-based models 33. In particular, an error hypothesis can thus be specified, the verification of which can be carried out automatically by means of the system.
Die Hypothesenverarbeitungseinrichtung 20 enthält eine mit Modell Engine bezeichnete Verarbeitungseinrichtung 21 für physikalische Modelle 31 und einen in Fig. 1 als RCA Modell Navigator bezeichneten Hypothesenbearbeiter 22. Die Verarbeitungs- einrichtung 21 greift zyklisch auf Prozeßdaten zu, die ein Datenserver 40 bereitstellt, führt eine Berechnung von Anlagen- und Prozeßzuständen nach Vorgabe eines physikalischen Modells 31 durch, und speichert das Ergebnis in einem Datenspeicherbereich für Berechnungsergebnisse 32. Der Hypothesenbearbeiter 22 greift beim Bearbeiten einer Hypothese auf diese Berechnungsergebnisse 32, sowie auf Checklisten der wissensbasierten Modelle 33 zu.The hypothesis processing device 20 contains a processing device 21 designated as a model engine for physical models 31 and a hypothesis processor 22 designated as an RCA model navigator in FIG. 1. The processing device 21 accesses process data cyclically, which a data server 40 provides, performs a calculation of System and process states according to the specification of a physical model 31, and stores the result in a data storage area for calculation results 32. The hypothesis processor 22 accesses these calculation results 32 as well as checklists of the knowledge-based models 33 when processing a hypothesis.
Der Datenspeicher 30 enthält Speicherbereiche mit Dateien, in denen die physikalischen Modelle 31 und wissensbasierten Modelle 33 abgelegt sind, und in denen die Berechnungsergebnisse 32 gespeichert werden.The data memory 30 contains memory areas with files in which the physical models 31 and knowledge-based models 33 are stored and in which the calculation results 32 are stored.
Fig. 2 zeigt das Verfahren zur automatischen Bearbeitung von Fehlerhypothesen mit Hilfe der in Fig. 1 generell, und in Fig. 3 beispielhaft dargestellten physikalischen Modelle. Der Feh leranalytiker navigiert zunächst zu einer Fehlerhypothese, um das Verfahren zu starten. In einem Verfahrensschritt 100 lädt der Hypothesenbearbeiter 22 die für die Verifikation der Hypothese erforderlichen Berechnungsergebnisse 32. In einem folgenden Schritt 200 lädt der Hypothesen bearbeiter 22 außerdem die Checkliste der betreffenden Hypothese aus den wissensbasierten Modellen 33. Fig 6 zeigt ein Beispiel für eine solche Checkliste. In einem Schritt 300 führt der Hypothesenbearbeiter 22 einen Abgleich der Berechnungsergebnisse mit den Checklistenpunkten der Checkliste durch. Die Checklistenpunkte, für die Modelle hinterlegt sind, werden dabei automatisch ausgewertet. Jeder Checklistenpunkt enthält eine Bedingung zur Verifikation der Hypothese. In einem Schritt 400 wird durch den Hypothesenbearbeiter 22 gekennzeichnet, ob der Checklistenpunkt die Bedingung erfüllt o- der nicht erfüllt. Fig. 7 zeigt beispielhaft, wie eine Checkliste nach der Bearbeitung ausgegeben wird. Fig. 3 zeigt beispielhaft das physikalische Modell eines chemischen Prozesses in einem Reaktor. Das Modell ist in Form einer Differentialgleichung angegeben. Das Modell beschreibt die Prozeßparameter im fehlerfreien Zustand. Ein Fehler kann mit solch einem Modell durch den Vergleich des berechneten Parameters mit dem real gemessen Wert ermittelt werden. Beispielsweise können die Eintritts- und Austrittstemperaturen des Kühlwassers berechnet werden. Weicht die berechnete Austrittstemperatur vom gemessenen Wert ab, kann mit einem entsprechenden Gleichungssystem unter Beachtung bestimmter Randbedingungen auf einen Meßwertfehler geschlossen werden. Mit Hilfe der angegebenen Differentialgleichung kann z.B. mit T0 der Temperaturmeßfehler und mit V eine Leckage diagnostiziert werden.FIG. 2 shows the method for the automatic processing of error hypotheses with the aid of the physical models shown in FIG. 1 in general and in FIG. 3 by way of example. The fault analyst first navigates to a fault hypothesis in order to start the method. In a method step 100, the hypothesis processor 22 loads the calculation results 32 required for verifying the hypothesis. In a subsequent step 200, the hypothesis processor 22 also loads the checklist of the relevant hypothesis from the knowledge-based models 33. FIG. 6 shows an example of such a checklist. In a step 300, the hypothesis processor 22 compares the calculation results with the checklist items on the checklist. The checklist items for which models are stored are automatically evaluated. Each checklist item contains a condition for verifying the hypothesis. In a step 400, the hypothesis processor 22 identifies whether the checklist item meets or does not meet the condition. 7 shows an example of how a checklist is output after processing. 3 shows an example of the physical model of a chemical process in a reactor. The model is given in the form of a differential equation. The model describes the process parameters in an error-free state. With such a model, an error can be determined by comparing the calculated parameter with the real measured value. For example, the inlet and outlet temperatures of the cooling water can be calculated. If the calculated outlet temperature deviates from the measured value, an appropriate system of equations can be used to deduce a measurement value error taking certain boundary conditions into account. With the help of the specified differential equation, the temperature measurement error can be diagnosed with T 0 and a leak with V, for example.
Fig. 4 zeigt die prinzipielle Darstellung eines Fehlermodells als wissensbasiertes Modell 33. Die dargestellte oberste Ebene beinhaltet ein Prozeßmodeil mit seinen Prozeßschritten. Jeder Prozeßschritt kann in weitere Prozeßschritte untergliedert sein. Zu jedem Prozeßschritt gibt es Fehlerereignisse und kritische Komponenten. Dazu gibt es wiederum Fehlerbäume mit Knoten. Die Knoten eines Fehlerbaumes repräsentieren Fehlerhypothesen. Wesentlicher inhaltlicher Bestandteil einer Fehlerhypothese ist eine Checkliste zur Verifikation. Auf die Inhalte einer Hypothese wird in Fig. 5 näher eingegangen.FIG. 4 shows the basic representation of an error model as a knowledge-based model 33. The top level shown contains a process model with its process steps. Each process step can be subdivided into further process steps. There are error events and critical components for each process step. In addition there are error trees with nodes. The nodes of a fault tree represent fault hypotheses. A checklist for verification is an essential part of the content of an error hypothesis. The contents of a hypothesis are discussed in more detail in FIG. 5.
Fig. 5 zeigt die Struktur eines Fehlerbaums. Das Modell hat einen hierarchischen Aufbau und enthält in der einfachsten Ausprägung zwei Ebenen. Die oberste Ebene repräsentiert das Fehlerereignis. Einem Fehlerereignis können mehrere Fehlerhypothesen unterlagert sein. Die logische Abhängigkeit kann folgendermaßen formuliert werden: Ein oder mehrere Fehlerhypothesen können Ursache für das Fehlerereignis sein. Fehlerereignis und Fehlerhypothese haben eine ähnliche inhaltliche Beschreibung. Die Fehlerhypothese kann zur tiefergehenden Analyse auf andere Fehlermodelle verweisen, d.h. ein Fehlerbaum kann sich aus mehreren Teilbäumen zusammensetzen. Die Verbindung wird durch das Attribut Fehlerbaumreferenz hergestellt.5 shows the structure of a fault tree. The model has a hierarchical structure and contains two levels in its simplest form. The top level represents the error event. Several fault hypotheses can be subordinate to one fault event. The logical dependency can be formulated as follows: One or more error hypotheses can be the cause of the error event. Error event and error hypothesis have a similar content description. The error hypothesis can refer to other error models for in-depth analysis, i.e. a fault tree can consist of several subtrees. The connection is established using the fault tree reference attribute.
Fig. 6 zeigt beispielhaft wie das System die Fehlerhypothese "Energiezufuhr zu hoch" einem Benutzer präsentiert. Eine Beschreibung der Fehlerhypothese erklärt dabei den Zusammenhang zwischen Fehler und möglicher Ursache. Eine Lokalisierung gibt den möglichen Fehlerort an; im Beispiel ist dies der Reaktor XY. Die Hypothese wird verifiziert, indem eine Verifikationscheckliste abgearbeitet wird. Die Prüfungen "Fehler Temperaturmessung" und "Leckage zum Kühlmantel" können durch ein physikalisches Modell automatisch verifiziert werden, eine Fehlerbaumreferenz ermöglicht für das Diagnosekriterium "Falsche Bedienanleitung" zur tieferen Diagnose den Zugang zu einem zugehörigen Fehlerbaum.6 shows an example of how the system presents the error hypothesis "energy supply too high" to a user. A description of the error hypothesis is explained the connection between the error and the possible cause. A localization indicates the possible fault location; in the example this is the reactor XY. The hypothesis is verified by working through a verification checklist. The tests "Temperature measurement error" and "Leakage to the cooling jacket" can be automatically verified by a physical model. An error tree reference enables access to an associated error tree for the diagnostic criterion "Incorrect operating instructions" for deeper diagnosis.
Fig. 7 zeigt beispielhaft, wie eine automatisch verifizierte Checkliste dargestellt wird. Bereits automatisch negativ verifizierte Diagnosekriterien sind dabei kursiv dargestellt. Ein positiv verifiziertes Diagnosekriterium wird fett dargestellt und mit Ausrufezeichen hervorgehoben. Noch zu prüfende Diagnosekriterien sind fett und mit Fragezeichen dargestellt. 7 shows an example of how an automatically verified checklist is displayed. Diagnostic criteria that have already been automatically negatively verified are shown in italics. A positively verified diagnostic criterion is shown in bold and highlighted with an exclamation mark. Diagnostic criteria to be checked are shown in bold and with question marks.

Claims

Patentansprüche claims
1. Verfahren zur automatisierten Bearbeitung von Fehlerhypothesen im Rahmen einer Fehlerursachenanalyse im Fall eines Fehlerereignisses in einer technischen Anlage, wobei1. Process for the automated processing of error hypotheses in the context of an error cause analysis in the event of an error event in a technical system, whereby
a) ein Datenverarbeitungssystem (1) verwendet wird, in dem physikalische Modelle (31) von Anlagenfunktionen und Prozessen, die mittels der Anlage durchführbar sind, und wissensbasierte Modelle (33) zur Fehlerursachenanalyse, Mittel (21 , 32) zur Berechnung und Speicherung von Anlagen- und Prozeßzuständen unter Zugriff auf die physikalischen Modelle (31) und auf Daten, die in einem Datenserver (40) der technischen Anlage gespeichert sind, sowie Mittel zur Hypothesenbearbeitung (22) und eine Ein/Ausgabe-Einrichtung (11) vorhanden sind, unda) a data processing system (1) is used in which physical models (31) of plant functions and processes that can be carried out by means of the plant, and knowledge-based models (33) for analyzing the causes of faults, means (21, 32) for calculating and storing plants - And process states with access to the physical models (31) and to data that are stored in a data server (40) of the technical system, as well as means for hypothesis processing (22) and an input / output device (11), and
b) nach der Vorgabe einer Fehlerhypothese durch einen Benutzer des Systems (1), das Mittel zur Hypothesenbearbeitung (22) unter Zugriff auf Ergebnisse einer Berechnung von Anlagen- und Prozeßzuständen, sowie unter Zugriff auf eine Checkliste der wissensbasierten Modelle (33), automatisiert eine Verifikation der Fehlerhypothese anhand von Bedingungen durchführt, die Checklistenpunkten der Checkliste zugeordnet sind, in einer Ergebnisliste das Verifikationsergebnis je Checklistenpunkt einträgt, und eine Ausgabe der Ergebnisliste bewirkt.b) after the specification of an error hypothesis by a user of the system (1), the means for hypothesis processing (22) with access to the results of a calculation of system and process states, and with access to a checklist of knowledge-based models (33), automates one Verifies the error hypothesis on the basis of conditions that are assigned to checklist items in the checklist, enters the verification result for each checklist item in a result list, and outputs the result list.
2. System zur automatisierten Bearbeitung von Fehlerhypothesen im Rahmen einer Fehlerursachenanalyse im Fall eines Fehlerereignisses in einer technischen Anlage, wobei ein Datenverarbeitungssystem (1) vorhanden ist, in dem physikalische Modelle (31) von Anlagenfunktionen und Prozessen, die mittels der Anlage durchführbar sind, und wissensbasierte Modelle (33) zur Fehlerursachenanalyse, Mittel (21 , 32) zur Berechnung und Speicherung von Anlagen- und Prozeßzuständen unter Zugriff auf die physikalischen Modelle (31) und auf Daten, die in einem Datenserver (40) der technischen Anlage gespeichert sind, sowie Mittel zur Hypothesenbearbeitung (22) und eine Ein/Ausgabe-Einrichtung (11) enthalten sind. 2.System for the automated processing of fault hypotheses as part of a fault cause analysis in the event of a fault event in a technical installation, a data processing system (1) being present in which physical models (31) of plant functions and processes which can be carried out by the plant, and knowledge-based models (33) for error cause analysis, means (21, 32) for calculating and storing plant and process states while accessing the physical models (31) and data stored in a data server (40) of the technical plant, and Means for hypothesis processing (22) and an input / output device (11) are included.
3. System nach Anspruch 2, dadurch gekennzeichnet, daß das Datenverarbeitungssystem (1) dafür eingerichtet ist, daß nach der Vorgabe einer Fehlerhypothese durch einen Benutzer, das Mittel zur Hypothesenbearbeitung (22) unter Zugriff auf Ergebnisse einer Berechnung von Anlagen- und Prozeßzuständen, sowie unter Zugriff auf eine Checkliste der wissensbasierten Modelle (33), automatisiert eine Verifikation der Fehlerhypothese anhand von Bedingungen durchführt, die Checklistenpunkten der Checkliste zugeordnet sind, in einer Ergebnisliste das Verifikationsergebnis je Checklistenpunkt einträgt, und eine Ausgabe der Ergebnisliste bewirkt. 3. System according to claim 2, characterized in that the data processing system (1) is set up so that after the specification of an error hypothesis by a user, the means for hypothesis processing (22) with access to the results of a calculation of plant and process states, and with access to a checklist of the knowledge-based models (33), automatically carries out a verification of the error hypothesis on the basis of conditions that are assigned to checklist items of the checklist, enters the verification result for each checklist item in a result list, and outputs the result list.
EP02777189A 2001-09-24 2002-09-24 Method and system for processing fault hypotheses Withdrawn EP1451689A2 (en)

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