WO2020216452A1 - Analyse de l'état d'une installation - Google Patents

Analyse de l'état d'une installation Download PDF

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Publication number
WO2020216452A1
WO2020216452A1 PCT/EP2019/060778 EP2019060778W WO2020216452A1 WO 2020216452 A1 WO2020216452 A1 WO 2020216452A1 EP 2019060778 W EP2019060778 W EP 2019060778W WO 2020216452 A1 WO2020216452 A1 WO 2020216452A1
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WO
WIPO (PCT)
Prior art keywords
training
parameter set
computing unit
parameters
computer system
Prior art date
Application number
PCT/EP2019/060778
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German (de)
English (en)
Inventor
Schirin Tolksdorf
Fabian Witt
Christian Würfel
Original Assignee
Siemens Aktiengesellschaft
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 Siemens Aktiengesellschaft filed Critical Siemens Aktiengesellschaft
Priority to PCT/EP2019/060778 priority Critical patent/WO2020216452A1/fr
Publication of WO2020216452A1 publication Critical patent/WO2020216452A1/fr

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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
    • G05B19/00—Programme-control systems
    • G05B19/02—Programme-control systems electric
    • G05B19/04—Programme control other than numerical control, i.e. in sequence controllers or logic controllers
    • G05B19/042—Programme control other than numerical control, i.e. in sequence controllers or logic controllers using digital processors
    • 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
    • G05B23/0221—Preprocessing measurements, e.g. data collection rate adjustment; Standardization of measurements; Time series or signal analysis, e.g. frequency analysis or wavelets; Trustworthiness of measurements; Indexes therefor; Measurements using easily measured parameters to estimate parameters difficult to measure; Virtual sensor creation; De-noising; Sensor fusion; Unconventional preprocessing inherently present in specific fault detection methods like PCA-based methods
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Definitions

  • the invention relates to a method for analyzing the status of a plant, wherein a sequence of measurement data of at least one measured variable, which depends on a status of the plant, is recorded by means of an interface of a computer system.
  • the invention also relates to a method for providing a set of parameters, a status analysis system, a training system and associated computer programs.
  • Hardware is usually required for an evaluation of industrial systems, for example an energy efficiency evaluation
  • this object is achieved by a method for analyzing the status of a system, a method for providing a parameter set, a status analysis system, a training system and computer programs according to the independent claims.
  • Advantageous further developments and further embodiments are the subject of the dependent claims.
  • a method in particular an at least partially wise computer-implemented method for analyzing the condition of a system, wherein a sequence of measurement data of at least one measured variable, which depends on a condition of the system, is recorded by means of an interface of a computer system, in particular according to a predetermined set of parameters. At least two statistical parameters are determined based on the sequence of measurement data by means of a computing unit of the computer system according to the specified parameter set. The statistical parameters are weighted by means of the computing unit according to the parameter set. A classification analysis is carried out by means of the arithmetic unit in accordance with the parameter set and based on the weighted statistical parameters. By means of the computing unit, the condition of the installation is determined based on a result of the classification analysis.
  • the system can in particular include an industrial system, a machine, an electrical machine, a pneumatic machine, a heat engine, an electrical consumer or an energy consumer.
  • state can be understood to mean, for example, a current operating state of the system.
  • state is not to be understood as an aging state or as a state in relation to an expected service life of the system.
  • the at least one measured variable can in particular contain a measured variable of the system, in particular a state variable or an operating variable of the system.
  • the at least one measured variable can in particular contain an energy-related variable, for example electrical power consumption, electrical voltage or electrical current intensity.
  • the at least one measured variable can also contain a converted heat, a thermal output, an operating pressure, a pneumatic energy variable, an operating temperature, an ambient temperature, an insulating medium temperature or a temperature of a component of the system.
  • the sequence of measurement data can be stored, for example, by means of the computing unit or another computing unit, for example on a storage medium of the computer system, in particular in order to carry out the acquisition of the following method steps.
  • the sequence of measurement data can, for example, correspond to a work section, in particular a time work section of the system.
  • the sequence of measurement data can include values of the at least one measured variable measured during the section.
  • the sequence can be understood to mean an ordered set of N-tuples of the at least one measured variable, where N corresponds to a number of measured variables which are comprised by the at least one measured variable, and N can be greater than or equal to one. Each N-tuple then corresponds to a measurement data item in the sequence.
  • the at least two statistical parameters can be determined according to the specified parameter set can be understood in such a way that the at least two statistical parameters are determined using one or more parameters of the parameter set.
  • at least two initial statistical parameters based on the sequence of measurement data can be determined by means of the computing unit independently of the parameter set.
  • the initial statistical parameters can then, for example, be scaled or normalized using one or more scaling parameters or scaling factors that are included in the parameter set in order to obtain the two statistical parameters.
  • the classification analysis includes, in particular, the application of a classification method to the at least two weighted statistical parameters.
  • the classification analysis or the classification process can assign the sequence, in particular the section, to one of several, in particular predetermined, classes.
  • the classification analysis can use one or more classification parameters included in the parameter set, for example.
  • the assignment of the sequence to the class can in particular be unambiguous, that is to say the sequence is assigned to exactly one class from a predetermined set of classes according to the classification method.
  • an interface can in particular contain a hardware interface and / or a software interface of the computer system.
  • the interface can in particular contain a PCI bus, a USB or a Firewire interface.
  • a computing unit can have hardware elements, for example one or more microprocessors, and / or software elements.
  • a storage medium can, in particular, be a non-permanent main memory, for example a random access memory, or a permanent mass storage medium, for example a hard drive, a flash memory, an SD card or a solid-state memory. State disk included.
  • the status of the system can take place without intervening in the system or machine control.
  • existing systems can also be analyzed.
  • the condition of the plant determined by means of the method for condition analysis can be used, for example, to evaluate the plant.
  • a key performance indicator (KPI) in particular for the section concerned, an energy efficiency assessment or a potential assessment for the system can be carried out.
  • the result of the status analysis can be used for process optimization of the system be used.
  • the at least one measured variable contains a measured variable with regard to electrical or pneumatic energy, with regard to a pressure, an electrical current or a power, in particular an electrical power.
  • the state is, for example, an energetic state, for example an energetic level, an energy level, a power level, a level of power consumption by the system or a state defined by characteristic signatures in the course of the at least one measured variable.
  • the at least two statistical parameters in particular for each of the at least one measured variable, contain a maximum value of the at least one measured variable, a minimum value of the at least one measured variable, an average value of the at least one measured variable, a variance of the at least one measured variable , a standard deviation of the at least one measured variable, a sum of the values, and / or a sum of the absolute values of the few At least one measured variable or one of the named parameters scaled according to the specified set of parameters.
  • the at least two statistical parameters in particular for each of the at least one measured variables, contain a sum of all absolute changes in the at least one measured variable, a sum of all positive changes in the at least one measured variable, and a sum of all negative changes in the at least one measurement size, a number of measured values above the mean value of the at least one measured variable, a maximum number of successive measuring points above the mean value of the at least one measured variable and / or a maximum number of consecutive measured values below the mean value of the at least one measured variable or one according to the specified Scaled parameter set of the parameters mentioned.
  • the at least one measured variable is measured by at least one sensor, in particular of the computer system, in order to generate the sequence of measured data.
  • the at least one sensor is in particular connected or can be connected to the interface and provides the measurement data so that they can be recorded by means of the interface.
  • the at least one sensor contains an energy measuring device, an energy meter, an ammeter, a voltage measuring device, a temperature sensor, a pressure sensor or a heat sensor.
  • the specified parameter set contains a length of the sequence and / or a set of weighting factors for weighting the statistical parameters and / or a classification parameter for the classification analysis.
  • the length of the sequence is, for example, a number of measuring points or N-tuples within the sequence, so to speak the duration of a recording time period for recording the measuring points of the sequence.
  • the recording period can be referred to as a window or a data window, for example.
  • the acquisition of the sequence of measurement data takes place according to the parameter set
  • the acquisition takes place in particular according to the length of the sequence. This means, for example, that the at least one sensor from the
  • the computing unit or a control unit is controlled according to the length of the sequence in order to record a corresponding number of measuring points or to carry out the measurement over a corresponding period of time.
  • the sensor can measure the at least one measured variable continuously and / or permanently or continuously, in particular over several sections, and the detection itself by the interface takes place according to the length of the sequence.
  • the parameter set can also contain one or more scaling parameters or scaling factors for scaling the initial statistical parameters.
  • a reliable status analysis in particular an energy-related status, of the system can be achieved.
  • two or more of the at least two statistical parameters are assigned to a given feature block and the weighting of the two or more statistical parameters assigned to the feature block is assigned a common weighting factor, in particular a common weighting factor for the feature block of the parameter set.
  • the fact that the two or more parameters are assigned to the feature block can be understood to mean that it is predefined that the two or more of the at least two statistical parameters belong to the specified feature block, so that the assignment itself is not necessarily part of the method.
  • the feature block and / or further feature blocks can be weighted differently, for example.
  • each of the at least two statistical parameters is assigned to one, in particular exactly one, feature block of a predetermined set of two or more feature blocks.
  • the weighting of the statistical parameters assigned to the respective feature blocks is then carried out with a respective common weighting factor of the respective feature block.
  • the classification analysis by means of the computing unit identifies a class, in particular a class for the sequence, and a probability for the class is determined.
  • the identified class together with the probability can be viewed, for example, as the result of the status analysis.
  • the class that is identified corresponds, for example, to the state of the system and the probability corresponds to a probability with which the system is actually in the identified state or in the identified class during the section belonging to the sequence.
  • post-processing of the result of the classification analysis is carried out by means of the computing unit if the probability for the identified class is less than a predefined threshold value.
  • the state can then be determined based on the result of the post-processing.
  • a method for providing a parameter set in particular for a method for analyzing the condition of a plant, in particular according to the improved concept, is given.
  • a large number, i.e. three or more, of training sequences of measurement data of at least one measured variable, which depends on a state of a training system is recorded by means of a first interface of a training computer system.
  • a first, a second and a third step are carried out by means of an arithmetic unit of the training computer system according to a predetermined initial set of parameters.
  • at least two statistical parameters based on the measurement data of the respective training sequence are determined by the computing unit.
  • the statistical parameters, in particular the respective training sequence are weighted by means of the computing unit, in particular based on the initial parameter set.
  • a classification analysis is carried out by means of the computing unit, in particular based on the initial set of parameters, based on the weighted statistical parameters, in particular the respective training sequence.
  • Evaluation data of a result of the classification analyzes, in particular the classification analyzes for all of the training follow is recorded by means of a second interface of the training computer system.
  • an adapted parameter set is generated depending on the evaluation data, in particular by adapting the initial parameter set.
  • the first, the second and the third step are repeated for each of the training sequences according to the adapted parameter set, in particular if the adapted parameter set was generated as a function of the evaluation data.
  • the evaluation data of the results of the classification analyzes can in particular relate to the respective results of the respective classification analyzes of individual training sequences or an overall result of all classification analyzes.
  • the steps are carried out in accordance with the initial set of parameters, in particular, in that each of the steps is carried out in accordance with the set of parameters.
  • Steps for each of the training sequences in accordance with the adapted parameter set include, in particular, performing the steps in accordance with the adapted parameter set instead of the initial parameter set.
  • the acquisition of the evaluation data is also repeated, for example after the repetition of the steps in accordance with the adapted set of parameters.
  • the acquisition of the evaluation data and the repetition of the first, second and third steps with the adjusted parameter set are repeated iteratively until the evaluation data prove that the result of the classification analyzes is sufficient.
  • the last used adjusted parameter set can be output or made available, for example by means of a third interface of the computing unit. in particular to be used as a predetermined parameter set of a method for condition analysis according to the improved concept.
  • Whether the result of the classification analyzes is sufficient can be determined, for example, automatically and / or based on an assessment by a human user.
  • the training system can in particular be a system that is identical or similar or comparable to a system whose state is obtained by means of a state analysis according to the improved concept based on a given parameter set by a method for providing a parameter set according to the improved concept should be analyzed.
  • the training system can also be the same system.
  • the second interface can be, for example, an internal or software interface of the training computer system. If the evaluation data is generated by a human user, the second interface can be an external or hardware interface or a user interface or a
  • the training sequences can, for example, be generated analogously to the generation of the sequence for a method for state analysis according to the improved concept.
  • the at least one measured variable can be recorded over a certain period of time, for example by means of a sensor.
  • the recorded measured variables can then be divided into sections each section corresponds to one of the training sequences.
  • the at least one measured variable is measured by at least one sensor, for example of the training computer system, in order to generate the plurality of training sequences.
  • the initial parameter set contains a length of the training sequences, the lengths of all training sequences being the same, and / or a set of weighting factors for weighting the at least two statistical parameters and / or one or several classification parameters for classification analysis.
  • two or more of the at least two statistical parameters are assigned to a predetermined feature block and the weighting of the two or more statistical parameters assigned to the feature block is carried out with a common weighting factor of the initial parameter set.
  • a class for the training sequence is identified by the classification analysis of a respective training sequence by means of the computing unit and a probability for the respective identified class is determined.
  • the parameters which are contained in the initial parameter set or the values of the parameters in the initial parameter set can, for example, correspond to empirically determined or at least partially randomly selected starting values for the corresponding parameters.
  • the values of the initial parameters of the initial parameter set can be determined by user inputs, for example relating to a system or machine type or a work cycle of the system or expected states of the system or a question for the status analysis.
  • the weighting factors of the initial parameter set can be determined from the question.
  • a value for the length of the training sequences for the initial set of parameters can be determined from the work cycle and / or the system or machine type.
  • one or more classification parameters can be determined for the classification analyzes, for example a number of expected states or a number of classes.
  • the type of system can be, for example, a machine tool, a press machine or a robot.
  • the user input for determining the initial parameter set can accelerate a convergence or an overall duration of the method for providing the parameter set until the result of the classification analyzes is sufficient. But it is not absolutely necessary.
  • an unsupervised classification is carried out by means of the arithmetic unit according to the parameter set, in particular the initial parameter set and / or the adjusted parameter set, and based on the weighted statistical parameters.
  • the use of an unsupervised classification analysis or an unsupervised classification method advantageously means that no labeled or special training data set is required for the parameter set according to the improved concept to provide. This is particularly advantageous since the provision or generation of such a training data set is time-consuming and resource-intensive and can also require specific expert knowledge.
  • the unsupervised classification can include, for example, a K-means method, in particular a fuzzy K-means classification method.
  • the initial set of parameters can contain a number of classes as classification parameters, for example.
  • a user input is recorded by means of the second interface in order to record the evaluation data.
  • a graphic representation of the result of the classification analyzes can be generated and output on a user interface.
  • a user can evaluate the graphical representation and, based on this, enter or provide the evaluation data at least partially via the second interface, so that they can be recorded by means of the second interface.
  • the evaluation data provided by the user can in particular contain information on whether the results of the classification analyzes are considered sufficient or not.
  • it can also contain a qualified statement, for example about whether a larger number of classes should be used or whether an incorrect class assignment was made or whether the number of classes used is considered sufficient.
  • the adjustment of the parameter set can take place in a more targeted manner, so that a faster convergence of the parameter set is possible.
  • Acquiring the evaluation data by means of the user input has the advantage that it can be carried out quickly and easily and that an experience of the user can be used to advantage.
  • a visual recording of the graphic representation by a person is particularly effective and efficient.
  • the result of the classification analyzes is evaluated by means of the arithmetic unit or by means of a further arithmetic unit of the training computer system using a machine learning method, for example using an artificial neural network, in order to generate the evaluation data.
  • the artificial neural network can in particular be a folding neural network (English:
  • CNN convolutional neural network
  • the artificial neural network can be combined, for example, with an autoencoder to generate the evaluation data.
  • Particularly advantageous embodiments contain both the user input and the evaluation of the result of the classification analyzes using the artificial neural network for recording the evaluation data. In this way, an efficient yet particularly robust assessment can be made.
  • a comparison of marked time data series is carried out by means of analysis methods in order to acquire the evaluation data.
  • the specified parameter set was or is provided by means of a method for providing a parameter replacement according to the improved concept.
  • a condition analysis system with a computer system has an interface and a computing unit.
  • the interface is set up to acquire a sequence of measured data of at least one measured variable, which depends on a state of a system.
  • the computing unit is set up to determine at least two statistical parameters based on the sequence of measurement data in accordance with a predefined set of parameters.
  • the computing unit is set up to weight the statistical parameters according to the parameter set and to carry out a classification analysis according to the parameter set and based on the weighted statistical parameters.
  • the computing unit is also set up to determine the state of the installation based on a result of the classification analysis.
  • condition analysis system result directly from the method for condition analysis of a plant according to the improved concept and the corresponding configurations and vice versa.
  • the state analysis system is for performing a method for state analysis according to the improved Concept trained or programmed.
  • the method for state analysis is carried out by a state analysis system according to the improved concept.
  • a first computer program is specified.
  • the first computer program has commands which, when the first computer program is executed by a computer system, in particular by a computer system of a status analysis system according to the improved concept, cause the computer system to perform a method for status analysis of a system according to the improved concept.
  • a computer-readable storage medium is specified on which a first computer program according to the improved concept is stored.
  • a training system with a training computer system has a first and a second interface and a computing unit.
  • the first interface is set up to record a large number of training sequences of measurement data from at least one measurement variable, which depends on a state of a training system.
  • the processing unit is set up to carry out a first, a second and a third step for each of the training sequences in accordance with a predetermined initial parameter set.
  • the first step at least two statistical parameters are determined based on the measurement data of the respective training sequence.
  • the statistical parameters of the respective training sequence are weighted.
  • a classification analysis based on the weighted statistical parameters of the respective training sequence is carried out.
  • the second interface is set up to record evaluation data of a result of the classification analyzes, in particular of all training sequences, and the re-
  • the computer unit is set up to generate an adapted parameter set as a function of the evaluation data.
  • the computing unit is also set up to repeat the first, the second and the third step for each of the training sequences according to the adapted parameter set, in particular if the parameter set has been adapted as a function of the evaluation data.
  • the training system is designed or programmed to carry out a method for providing the parameter set according to the improved concept.
  • the method for providing the parameter set is carried out by the training system.
  • a second computer program is specified.
  • the second computer program has commands which, when the second computer program is executed by a computer system, in particular by a training computer system of a training system according to the improved concept, cause the computer system to carry out a method for providing a parameter set according to the improved concept.
  • a computer-readable storage medium is specified on which a second computer program according to the improved concept is stored.
  • FIG 2 shows an exemplary embodiment of a training system according to the improved concept and various aspects of an exemplary embodiment of a method for providing the parameter set according to the improved concept.
  • Figure 1 is a schematic representation of a state analysis system is shown according to the improved concept, wel Ches a computer system 1 has.
  • the computer system 1 is connected to one or more systems 6a, 6b, 6c in each case via a sensor 7a, 7b, 7c, for example an energy meter.
  • the computer system 1 has a computing unit 3, for example containing at least one microprocessor 4, and an interface 2a, 2b, 2c for connecting the computing unit 3 to the sensors 7a, 7b, 7c.
  • the computer system 1 is coupled to an input and output device 5, for example.
  • FIG. 2 shows a schematic representation of a training system based on the improved concept, which has a training computer system 9.
  • the training computer system 9 is connected to a training system 6d via a sensor 7d, for example an energy meter.
  • the training system 6d and the sensor 7d can, for example, be identical or structurally identical or comparable to one of the systems 6a, 6b, 6c and one of the sensors 7a, 7b, 7c.
  • the training computer system 9 has a processing unit 10, for example containing at least one microprocessor, and for connection to the transmitter sor 7d has a first interface 11.
  • the training computer system 9 is, for example, coupled via a second interface 12 to a user interface 13, which can serve as an input and output device.
  • the sensor 7d can record at least one measured variable, in particular an electrical power consumption and / or a current intensity, of the training system 6d and transmit it to the computing unit 10.
  • step 200 information such as a type, a work cycle or possible energetic states of the training system 6d can be recorded by the computing unit 10, for example through a user input or a database query.
  • the computing unit 10 can detect a question to be analyzed, for example through a user input or a database query, for example whether the energy levels of the training system 6d should be identified or whether recurring signature sections should be identified in the measured variable special regardless of the energy levels.
  • the values of the initial parameters of the initial parameter set can be determined by user inputs, for example relating to a system or machine type or a work cycle of the system or expected states of the system or a question for the status analysis.
  • the computing unit 10 can divide the recorded values for the measured variable into sections to be analyzed, for example depending on the work cycle of the system or a sampling rate when the measured variable is recorded. For example, a section length can vary with the work cycle be equated.
  • the values of the measured variable of the individual sections can be viewed as respective training sequences. The segment length, or the length of the training sequences, represents a given initial parameter.
  • the computing unit 10 can automatically perform a feature extraction, for example specifically for the respective question.
  • the feature extraction can be carried out based on, for example, empirically determined feature blocks. These feature blocks, which each contain one or more optionally scaled statistical parameters, can extract different information from the recorded measured variables.
  • the arithmetic unit can weight the feature blocks or the statistical parameters of the feature blocks differently and thereby select how much the individual feature blocks should be taken into account in order to answer the question.
  • the training system begins, for example, with an initial set of weighting parameters, empirically determined for example for the question, and corrects these if necessary in the further course of the training.
  • the parameters which, optionally scaled by means of one or more initial scaling parameters, can be assigned to the feature blocks, can contain for each section: a maximum value of the measured variable, a minimum value of the measured variable, a mean value of the measured variable, a variance of the measured variable, a standard deviation of the Measured variable, a sum of the values of the measured variable, a sum of all absolute changes in the measured variable ("absolute sum of changes"), a sum of all positive changes in the measured variable (“positive sum of changes”), a sum of all negative changes in the few at least one measured variable (“negative sum of changes”), a number of measured values above the mean value of the measured variable ("count above mean”), a maximum number of consecutive measuring points above the mean value of the measured variable ("longest strength above mean”) and / or a maximum number of consecutive measured values below the mean value of the measured variable (“Longest strike below mean”).
  • the scaled extracted features are placed in an unsupervised classifier in a step 700.
  • One possibility can be a fuzzy K-means classifier.
  • the classifier is trained, for example, with all recorded, preprocessed measurement data.
  • there can be different parameters which, if necessary, can be adapted differently from the parameters initially used.
  • a number of classes can be specified which also serves as an initial parameter. In the course of the training, this can also be iteratively adapted, for example, if a user wants a more detailed analysis of the data with more classes.
  • the classes can, for example, correspond to the energetic states of the training facility 6d, which, for example, have one or more of the states "Off", "Ready"
  • a system such as the training system 6d, for example, generally has at least three states, an initial number of classes equal to three can be specified, for example.
  • a result of the classification contains, for example, a class for each section and optionally a probability for the respective class.
  • the result can optionally be output to a user, for example in the form of a graphic see illustration 8, which in the example shown can contain an electrical power consumption P of the training system 6d as a function of time t.
  • the classes or states i, ii, iii, iv found in each case can be assigned to the sections, for example by means of color coding.
  • the result can, for example, be evaluated in a step 900 by means of a user interaction.
  • the user can, for example, confirm the result, i.e. evaluate it as sufficient or reject the result.
  • the user can provide additional information, for example whether the number of classes found is correct and / or whether the correct classes have been identified and / or whether a more detailed analysis with more classes is required or desired.
  • an automatic evaluation can take place in step 1000, for example using the computing unit 10 or a cloud, based on known successful analysis results of the same or similar systems.
  • a comparison of image files representing the respective results can be carried out using a folding neural network (CNN) or an auto-encoder with a subsequent neural network.
  • CNN folding neural network
  • step 1100 it can be checked whether a sufficiently good result is present. If this is not the case, the
  • Steps 400 to 800 and optionally 900 to 1100 are iteratively run through during the training phase, depending on the evaluation of the result, until a sufficiently good result is available.
  • the initial parameters can be replaced by adapted parameters and adapted again in each iteration. If the result is sufficiently good, the last used parameter set can be used in a step 1200 as a specified parameter set for status analysis. analysis of one or more of the systems 6a, 6b, 6c of FIG. 1 can be used or specified.
  • the senor 7a can be used to record a repeated, in particular continuous, recording of a measured variable, in particular an electrical power consumption and / or a current intensity, of the system 6a and transmit it to the computing unit 3.
  • the measurement data can be stored in a storage medium, for example the computing unit 3.
  • the computing unit 3 can be used to extract features.
  • At least two statistical parameters of a sequence corresponding to the stored measurement data, as described with regard to the training phase, are determined and scaled using scaling parameters of the specified parameter set.
  • the statistical parameters for example the feature blocks described above, are weighted with the weighting parameters according to the specified parameter set.
  • the weighted statistical parameters are subjected to a classification analysis by means of the computing unit, for example by a fuzzy K-means classifier, the classification parameters of the specified parameter set being used, for example the number of classes for the fuzzy K-means determined during the training
  • a class for example with a corresponding class probability, is determined and optionally output or used or further processed for evaluating the system 6a.
  • Post-processing of the classification can optionally take place, for example if the analyzed section could only be assigned to a class with a probability that is less than a predetermined limit value.
  • the output class can represent the status of the analyzed section and can in particular be passed on to a system that can use the information about the status to calculate, for example, KPIs per status, an energy efficiency or a potential estimate for the system 6a, or to optimize the operation of the system 6a.
  • condition of a system can be determined automatically without having to access its control.

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  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un procédé automatisé d'analyse de l'état d'une installation (6a, 6b, 6c). Une séquence de données de mesure d'au moins une grandeur mesurée du système (6a, 6b, 6c) est acquise au moyen d'une interface (2a, 2b, 2c) et au moins deux paramètres statistiques sont déterminés sur la base de la séquence au moyen d'une unité de calcul (3) selon un ensemble de paramètres spécifié. Les paramètres statistiques sont pondérés en fonction du jeu de paramètres et une analyse de classification est effectuée au moyen de l'unité de calcul (3) selon le jeu de paramètres et sur la base des paramètres statistiques pondérés. De plus, un état de l'installation (6a, 6b, 6c) est déterminé au moyen de l'unité de calcul (3) sur la base d'un résultat de l'analyse de classification.
PCT/EP2019/060778 2019-04-26 2019-04-26 Analyse de l'état d'une installation WO2020216452A1 (fr)

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6757668B1 (en) * 1999-11-05 2004-06-29 General Electric Company Information fusion of classifiers in systems with partial redundant information
DE102011117974A1 (de) * 2011-11-08 2013-05-08 Robert Bosch Gmbh Verfahren zur Überwachung eines Zustands einer Maschine und Überwachungssystem
DE102017000955A1 (de) * 2016-02-09 2017-08-10 Fanuc Corporation Produktionssteuersystem und integriertes Produktionssteuersystem
CA3041513A1 (fr) * 2016-10-24 2018-05-03 Fisher Controls International Llc Analyse de series chronologiques pour evaluation de la sante d'une soupape de commande
DE102017011544A1 (de) * 2016-12-14 2018-06-14 Fanuc Corporation Steuerung und maschinelle Lernvorrichtung
US20190101898A1 (en) * 2017-10-02 2019-04-04 Fisher-Rosemount Systems, Inc. Projects within a Process Control Asset Management System

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6757668B1 (en) * 1999-11-05 2004-06-29 General Electric Company Information fusion of classifiers in systems with partial redundant information
DE102011117974A1 (de) * 2011-11-08 2013-05-08 Robert Bosch Gmbh Verfahren zur Überwachung eines Zustands einer Maschine und Überwachungssystem
DE102017000955A1 (de) * 2016-02-09 2017-08-10 Fanuc Corporation Produktionssteuersystem und integriertes Produktionssteuersystem
CA3041513A1 (fr) * 2016-10-24 2018-05-03 Fisher Controls International Llc Analyse de series chronologiques pour evaluation de la sante d'une soupape de commande
DE102017011544A1 (de) * 2016-12-14 2018-06-14 Fanuc Corporation Steuerung und maschinelle Lernvorrichtung
US20190101898A1 (en) * 2017-10-02 2019-04-04 Fisher-Rosemount Systems, Inc. Projects within a Process Control Asset Management System

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