EP2297623A1 - Procédé de surveillance d'une installation industrielle - Google Patents

Procédé de surveillance d'une installation industrielle

Info

Publication number
EP2297623A1
EP2297623A1 EP09772536A EP09772536A EP2297623A1 EP 2297623 A1 EP2297623 A1 EP 2297623A1 EP 09772536 A EP09772536 A EP 09772536A EP 09772536 A EP09772536 A EP 09772536A EP 2297623 A1 EP2297623 A1 EP 2297623A1
Authority
EP
European Patent Office
Prior art keywords
detection
measurement data
elimination
data
measured data
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Withdrawn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
EP09772536A
Other languages
German (de)
English (en)
Inventor
Hajrudin Efendic
Gerald Hohenbichler
Stephan M. Winkler
Andreas Schrempf
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.)
Primetals Technologies Austria GmbH
Original Assignee
Siemens VAI Metals Technologies GmbH Austria
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 VAI Metals Technologies GmbH Austria filed Critical Siemens VAI Metals Technologies GmbH Austria
Publication of EP2297623A1 publication Critical patent/EP2297623A1/fr
Withdrawn legal-status Critical Current

Links

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
    • G05B23/0221—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
    • 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
    • G05B23/0254—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 quantitative model, e.g. mathematical relationships between inputs and outputs; functions: observer, Kalman filter, residual calculation, Neural Networks

Definitions

  • the present invention relates to a method for the pretreatment of measurement data for monitoring an industrial plant.
  • the invention relates to a method for monitoring systems, in particular complex systems of the iron and steel industry, with the steps of recording at least two channels of measurement data of a system, if necessary
  • Target channel f) use of currently determined measurement data and of the model formed to calculate a simulated value of the target channel, g) detection of false states by comparing between the current and the simulated value of the target channel.
  • Modern industrial plants for example blast furnaces or rolling mills, typically with a large number of coupled individual plants, are highly complex technical systems.
  • Today, hundreds of thousands of measuring sensors are permanently and real-time monitored (eg with a sampling time of 1 ms) to monitor these systems or processes, and special relevant data is displayed by a measuring data acquisition system. Due to the high demands on the operating personnel, however, FD is increasingly used in complex systems, since these methods allow computer-aided errors in the computer system
  • FD is also in the condition monitoring, engl.
  • Condition monitoring used by industrial equipment.
  • a system is monitored by means of a large number of measuring sensors and automatically generates a warning or error message when the system behavior changes, for example as a result of wear of components.
  • the measured data are typically various plant or process conditions, which are determined by sensors and usually in digital form of a
  • a channel of measured data is a series of measured values recorded by a sensor;
  • a destination channel is a channel of recorded video
  • Measurement data that contains or may contain relevant information about the behavior of the plant.
  • a model for the target channel based on one or more channels (excluding one target channel) of plant measurement data is created; By means of this model and depending on current measurement data, the values of a simulated target channel are calculated by a process computer and compared with current measured values of the target channel; if there are significant deviations between the simulated and the measured target channel, an error message is generated.
  • Data preprocessing pointed out in the course of the FD. Specifically, it is proposed to filter the measurement data to eliminate disturbances in the measurement data and thus to improve the performance of FD.
  • the object of the invention is to provide a method for monitoring of industrial plants, with which the quality of the recorded measurement data of the system improved and the size of the measurement data can be greatly reduced, without resulting in a significant loss of information.
  • offline ie.
  • FD fault conditions of the system
  • outliers are detected in the measurement data and then eliminated.
  • the elimination of an outlier occurs by replacing an outlier with an average of the affected channel.
  • the measurement data channels are smoothed, for example by the use of median filters, resulting in e.g. Measurement noise is reduced and, as in the detection and elimination of outliers, the quality of the measurement data is increased.
  • Data channel before downsampling determined and compared with the information content of a changed. With respect to the sampling time data channel. This results from downsampling, ie. by a reduction of the sampling frequency, no significant change in the information content, the sampling frequency is reduced, whereby a large reduction of the amount of data (a reduction of the sampling frequency by 50% reduces the amount of data also by 50%) is possible.
  • the measurement data are subjected to the process steps in the order of detection and elimination of "zero channels", detection and elimination of outliers, filtering and downsampling.This order results in a high quality of the measurement data and in a high efficiency of the inventive method.
  • the measurement data preferably after downsampling, are subjected to detection of stationary areas and elimination of non-stationary areas, which further reduces the size of the measurement data and the formation of simple, static
  • a further advantageous embodiment is that, for different target channels, the steps of defining a target channel from the measured data, pretreatment of the measured data and measurement data based formation of at least one model of the target channel per target channel are each performed at least once, and models formed thereby in the detection of malfunction of the system be used. As a result, a particularly comprehensive monitoring of the system is achieved.
  • the steps of defining a target channel from the measured data, pretreatment of the measured data and measurement-data-based formation of at least one model of the target channel on at least one process computer are performed in parallel for different target channels. This is especially in online operation, ie. in the implementation of the method on one of the system associated process computer, given an early model availability.
  • the parallelization can be carried out either by a plurality of tasks or threads on a process computer, and / or by division into several process computers.
  • the detection and elimination of outliers contains a univariate and a multivariate step.
  • the univariate method step is particularly suitable for detecting and eliminating comparatively large outliers in one channel independently of other channels.
  • the multivariate step the distance of the measured values of all channels at a time becomes the total distribution determined, which also difficult to detect outliers can be detected and eliminated.
  • the measured data are subjected to a median filtering.
  • Median filters are known in the art and allow a very efficient smoothing of signals.
  • the downsampling of the measured data takes place taking into account the
  • Transinformation engl. Auto mutual information, between a channel before and after downsampling. This makes it possible to reduce the sampling frequency as a function of the loss of information due to the downsampling and thus set an optimized downsampling rate.
  • a further advantageous embodiment is that the detection of stationary areas and elimination of non-stationary areas taking into account statistical variables for the variability.
  • the measured data preferably after the
  • Fig. 5 Plot of a section of the target channel xio before and after the step of detection and elimination of outliers
  • Fig. 7 Plot of a section of the channel X20 before and after
  • Step Filtering Fig. 8 Schematic representation of the downsampling
  • Fig. 9 plot of the target channel xio before and after the step
  • Fig. 13 Plot of the target channel xio before and after the step Detection of stationary areas and elimination of non-stationary areas and the channels xio, X20 and x. 7
  • the measurement data are, for example, signals from pressure, force, displacement, velocity, acceleration or temperature sensors, which were recorded for subsequent use in an FD method.
  • the channel xio was selected as the target channel (tenth column of MD).
  • the channels Xi to X20 are shown graphically above the measured data index.
  • the channels 9 and 17 were identified as zero channels and eliminated.
  • the dimension of the measurement data matrix after the step of detection and elimination of "zero channels" is 19498 x 18, ie the amount of data has been reduced to 90%.
  • the individual channels ie. the individual columns of the measurement data matrix
  • a univariate ie. based on only one channel
  • detection and elimination of outliers subject This eliminates "big" outliers.
  • the detection of outliers can be done in two ways:
  • test criterion G s is calculated for each measured value X 1 .
  • the one-sided test is an outlier
  • the check for outliers is analogous to the global procedure, but is limited to the viewing window.
  • Outliers in turn, have a measurement data matrix which is free of univariate outliers but still has the original dimension.
  • This process step can be performed following univariate detection and elimination of outliers.
  • any outliers due to the so-called Mahalanobis distance or the distance due to the so-called principal component analysis of a measured value vector x (a row vector of the measured data matrix) are detected by the overall distribution. Any outliers found are marked and replaced by local averages.
  • the Mahalanobis distance as well as the principal component analysis are e.g. known from
  • a principal component analysis of the measured data is carried out.
  • the main components of the measurement data matrix ie. the eigenvalues and eigenvectors, either via an eigenvalue analysis or a SVD (Singular value decomposition) of the covariance matrix is calculated.
  • Noise or other interfering signals are removed from individual channels of the measuring signals.
  • a median filter with a viewing window of size N (referred to as filter order) is used.
  • each measured value x k of a channel is given by _ _ (mean value (x k _ (N _ 1) / 2 , ..., x i + (7V _ 1) / 2 ) for odd N ⁇ mean value (x k _ N / 2 , ..., x k + N / 2 ) for even N replaced.
  • the downsampling of the measured data is carried out taking into account the transinformation (auto-mutual information, see chapter 3.3.1.3 "Entropy-oriented measures" from the gutter) AMI ( ⁇ ) between a channel before the downsampling and the same channel after the downsampling.
  • the transinformation AMI ( ⁇ ) is calculated from k
  • H (A) - ⁇ p (u j) • log 2 p (u j) the entropy of the channel A to Down Sampling
  • AMI ( ⁇ ) H (A) + H (A) - H (A, A) Transinformation between A and A
  • the optimal downsampling rate i with ie [r mn , T n ⁇ x ].
  • the optimal downsampling rate ie. that ⁇ , where as little information as possible is lost with the greatest possible data reduction, is the first local minimum of AMI ( ⁇ ), ie. AMI ( ⁇ -1)> AMI ( ⁇ ) ⁇ AMI ( ⁇ + 1). If such a minimum can not be found, then one uses a scaled one
  • Transinformation AMI ( ⁇ ) AMI ( ⁇ ) + e TF ° clor -1, so that a local minimum can be found.
  • the factor ⁇ Fa k to r is a scaling factor and can be selected appropriately.
  • the target channel is shown before and after downsampling, but of course, downsampling is applied to all channels.
  • FIG. 10 also shows the transinformation AMI ( ⁇ ) and the scaled transinformation AMI ( ⁇ ) * over ⁇
  • a redundancy signal cross-correlation between the target channel X and Y within the viewing window
  • RED (J) corr (X local J local ).
  • the redundant channels are deleted from the measurement data matrix.
  • a redundant channel to a destination channel already too represents a simple model of the target channel that can be used for error detection. Due to this process step, the measurement data matrix was reduced to 5.89% of the original data volume.
  • substantially constant operating points are to be identified from the measured data.
  • a viewing window with size NVAR is used, which is applied to the target channel.
  • This process step reduced the measurement data matrix to 4.4% of the original data volume.
  • the resulting measurement data matrix is the basis for a subsequent formation of at least one model for the target channel.
  • a process computer assigned to the installation calculates a simulated target channel, which is used for the comparison between the simulated and the measured target channel, on the basis of currently determined measurement data of the installation and the generated model. If there are significant deviations between these two channels, a warning or an error message is generated.
  • the detection of faulty states of a system can be carried out particularly comprehensively if not only one model of a destination channel is created, but for each of the different destination channels at least one model for the respective destination channel is formed and models formed thereby are used in the FD. Further steps, such as the identification or the isolation of errors, e.g. the publication
  • FIG. 14 shows in a flow chart the most important method steps in the pretreatment of the measured data.
  • at least two channels of measured data eg from different sensors, such as pressure, temperature, speed or force sensors
  • the originally present measurement data (1) in the pretreatment of the measurement data (7) are successively the method steps

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  • Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • Mathematical Physics (AREA)
  • Testing And Monitoring For Control Systems (AREA)

Abstract

L'invention concerne un procédé pour la surveillance d'installations, en particulier d'installations complexes de l'industrie sidérurgique, les étapes de ce procédé consistant à recevoir au moins deux canaux de données de mesure d'une installation, à stocker éventuellement lesdites données de mesure, à définir un canal cible à partir des données de mesure, à prétraiter les données de mesure, à créer sur la base des données de mesure au moins un modèle du canal cible, et à utiliser le modèle ainsi généré et les données de mesure courantes obtenues pour détecter les pannes de l'installation. Le but de la présente invention est de fournir un procédé de surveillance d'installations industrielles permettant d'améliorer l'installation grâce à la qualité des données de mesure relevées, et de réduire fortement le volume des données de mesure sans entraîner pour autant de pertes notables d'informations. Ce but est atteint par un procédé dans lequel les données de mesure sont soumises, lors de l'étape de prétraitement, aux opérations suivantes : 1) détection et élimination de canaux nuls, 2) détection et élimination des données aberrantes, 3) filtrage, et 4) sous-échantillonnage.
EP09772536A 2008-07-04 2009-07-03 Procédé de surveillance d'une installation industrielle Withdrawn EP2297623A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
AT0106008A AT507019B1 (de) 2008-07-04 2008-07-04 Verfahren zur überwachung einer industrieanlage
PCT/EP2009/058406 WO2010000836A1 (fr) 2008-07-04 2009-07-03 Procédé de surveillance d'une installation industrielle

Publications (1)

Publication Number Publication Date
EP2297623A1 true EP2297623A1 (fr) 2011-03-23

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ID=41010455

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EP09772536A Withdrawn EP2297623A1 (fr) 2008-07-04 2009-07-03 Procédé de surveillance d'une installation industrielle

Country Status (4)

Country Link
US (1) US20110106289A1 (fr)
EP (1) EP2297623A1 (fr)
AT (1) AT507019B1 (fr)
WO (1) WO2010000836A1 (fr)

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Also Published As

Publication number Publication date
WO2010000836A1 (fr) 2010-01-07
AT507019A1 (de) 2010-01-15
AT507019B1 (de) 2011-03-15
US20110106289A1 (en) 2011-05-05

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