WO2012048734A1 - Procédé de commande d'un processus industriel - Google Patents

Procédé de commande d'un processus industriel Download PDF

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Publication number
WO2012048734A1
WO2012048734A1 PCT/EP2010/065242 EP2010065242W WO2012048734A1 WO 2012048734 A1 WO2012048734 A1 WO 2012048734A1 EP 2010065242 W EP2010065242 W EP 2010065242W WO 2012048734 A1 WO2012048734 A1 WO 2012048734A1
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WO
WIPO (PCT)
Prior art keywords
data
selecting
input data
interval
output
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PCT/EP2010/065242
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English (en)
Inventor
Alf Isaksson
André CARVALHO BITTENCOURT
Krister Forsman
Daniel Peretzki
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Abb Research Ltd
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Priority to PCT/EP2010/065242 priority Critical patent/WO2012048734A1/fr
Publication of WO2012048734A1 publication Critical patent/WO2012048734A1/fr

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    • 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
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric

Definitions

  • the present invention concerns a method for controlling or monitoring an industrial process by use of a process model.
  • the method comprises the steps of loading data from data stored in a database during performance of the industrial process, and selecting input data intervals for estimating a process model.
  • the method further comprises the step of checking if input data within the selected data interval influence the process.
  • the present invention concerns a method for selecting a data interval for identifying an industrial process from a database comprising data logged during operation of an industrial process.
  • the invention relate to a system performing the methods and to a system with a processing unit in a computer based system and a computer program product .
  • Closed loop control may be applied to almost any process with input and output signals. Examples where closed loop control is widely used are refineries and other chemical or petrochemical processes, metals and mining industry, pulp and paper industry, food processing industry as well as control of power plants and heating and ventilation of buildings. Closed loop control is also often applied to control positions and movements in automated manufacturing processes or devices.
  • a traditional approach in the use of closed loop control is to measure a value of a process output and compare the measured value with a reference value.
  • closed loop control including setpoint regulation, tracking (time-varying reference trajectory), path following (varying reference independent of time) , disturbance attenuation etc.
  • PID control Proportional, Integral, Derivative
  • a sensor measurement serves as an input for a feedback control loop, and any difference between the measured sensor value and a reference value or signal, so called setpoint, is determined by a controller.
  • the controller then in turn sends signals to an actuator connected to the control loop in
  • controllers have to be tuned properly and, especially if the process is sensitive or dangerous, it is necessary to use a process model as accurate as possible.
  • model complexity is dependent on use of the model.
  • PI controller tuning which combines identifying a process model and application of some tuning method based on this model to find new PI parameters
  • simple models are often sufficient.
  • the model must represent the main dynamics in the relevant frequency band.
  • a model of a process can be obtained by use of an experiment.
  • the user designs a well planned experiment in order to collect data containing information about the system dynamics and properties of interest.
  • a sufficient model of the process might be possible to obtain from such experiment if the nature of the process is such that it can be excited for the purpose of process
  • models can be derived from physical principles.
  • process plants are mostly complex systems and therefore modeling from physical principles is often not feasible.
  • Such database contains plant
  • the data in such data base relates to different types of process situations, with more or less different information about the dynamics and characteristics of the process.
  • the operation of a process is normally in automatic mode and
  • a traditional way of finding excited data which as already explained above is a well established notion within system identification, is to detect steps with a sufficient size in the input signal, dependent on the range of the process values or on the estimated standard deviation of the noise.
  • Another computationally fast way is to estimate the variance of data for the input and output signals and compare the result with a predetermined threshold value which is exceeded if the data show significant excitation. Yet another method for
  • a suitable step size threshold has to be chosen which guarantees a response of the process, which could be difficult. Afterwards it is checked if some steps could be merged to a sequence of steps when they are close to each other. Therefore, a maximum gap in time between two steps is chosen. In the case that at least one large enough step/step-sequence is detected, the next step is to search for data samples where the manual input signal and the process output signal were in steady state both before and after the step occurred.
  • the method finds steady state by considering the ratio of the noise variance of the data samples for the output signal calculated in two different ways. The data is assumed to indicate steady state if the ratio of both variances is close to one. If the ratio is above a
  • the process output signal is considered not in steady state.
  • the reason for looking after steady state conditions before and after the step is to avoid possible disturbances. Therefore, additionally the time durations where the input signal and the process output signal were both
  • a last check is performed checking if the time delay of the process response to the step is no longer than a maximal permitted time delay. If the time delay is not too long, the stretch from the beginning of the step of the input signal to the sample where the process output signal returns into steady state is marked as useful for process identification. The procedure is then repeated with every found step, or sequence of steps, for further manual mode intervals found in the data.
  • condition number of the information matrix By calculating the condition number of the information matrix it is possible to check if the data is exciting enough to be informative of the process. A low condition number indicates excited data.
  • three criteria need to be fulfilled. The first two criteria require the variances of the input and output signals to exceed a respective predetermined threshold value. For the third criterion to be fulfilled, the condition number of the information matrix must fall below a predetermined threshold value which indicates sufficiently exciting data.
  • the method searches the previous data sample where the variance exceeded the low
  • the aim of the present invention is to remedy one or more of the above mentioned problems. This and other aims are obtained by a method defined by claim 1.
  • a method for controlling an industrial process by use of a process model.
  • the present method comprises the steps of loading data from sampled signals logged during performance of the industrial process and stored in a database, selecting at least one data interval with input data having sufficient excitation to be suitable for estimating model of the process and checking if the exciting input data has a sufficient influence on the process output to be suitable for estimating a model of the process.
  • the steps of selecting and checking are thereafter repeated to select more suitable data intervals.
  • a task when it comes to control of industrial processes is to identify simple low-order models obtainable from data intervals relating to either reference changes when the process control is in automatic mode, or controller output changes when the process control is in manual mode. If useful data intervals for system identification are provided, the user may decide which type of suitable model to be estimated. In the present method, start and end time as well as a quality attribute of the selected data intervals are provided. The present method provides reliable determination of excitation in the signals and handle problems related to disturbances. Further, the method is verified by experience from real examples of estimated process models.
  • variance is a simple and fast mathematical operation with no need for extremely powerful data systems. Further, if a large number of data samples are loaded, a lot of time is saved if simple calculations may be used in a first place. A sufficient result of such calculation is a strong indication that such data show sufficient excitation to be useful.
  • the variance of output data in the selected interval is calculated, whereby excitation of data is indicated if the variance exceeds a predetermined threshold value.
  • a selected data interval needs to comprise exciting process output data to be useful. If no excitation of the process output data is indicated for the data interval selected, the process response in the interval is too poor.
  • calculation of variances are fast and simple and thus provide a time saving method to select or to discard a data interval.
  • a method is disclosed where, in the step of selecting, an off-set value is subtracted from the exciting input data, and data where the result of the subtraction differs from zero is selected. By such operation, unwanted transients are avoided when filtering the input data.
  • a method is disclosed where, in the step of selecting, a data interval where the process is controlled in manual operating mode is selected.
  • a method is disclosed where, in the step of selecting, a data interval where the process is controlled in automatic operating mode is
  • a method is disclosed where, in the step of loading, the input data are filtered, whereby high frequencies are filtered out.
  • a method is disclosed where, in the step of loading, the input data is filtered whereby an approximate time delay of the input data is obtained.
  • a method is disclosed where, the input data is filtered through a cascaded series of filters, whereby the obtained time delay is dependent on the number of filters.
  • a method is disclosed where, in the step of checking, the correlation between the input data and the output data within the selected interval is verified. According to yet another embodiment of the invention a method is disclosed where, the correlation between the input data and the output data within the selected interval is verified by
  • Such statistical test may for example by a chi-square test.
  • a method comprising the further steps of estimating a process model with parameters obtained from the input data and output data within the selected data interval, and controlling the industrial process using the estimated process model.
  • a method for selecting, from a database comprising sampled signals logged during operation of an industrial process, a data interval suitable for identifying said industrial process.
  • the method comprises the steps of loading data the database, selecting at least one data interval with input data having sufficient excitation to be suitable for identifying said process, and checking if the exciting input data has a sufficient influence on the process output to be suitable for identifying said process. The steps of selecting and checking are then repeated to select more suitable data intervals.
  • the method aims to, in connection with process control and process modeling, provide a method selecting, from huge amounts of stored process data, intervals useful for process
  • the invention provides a method automatically searching and selecting intervals of data informative enough for process identification purposes.
  • the method according to the present invention provides relevant information to enable modeling an industrial process from a minimum previous knowledge of the process per se.
  • An advantage of the method according to the present invention is that manual errors are eliminated from the process of selecting data.
  • Another advantage is that in the case of hundreds or more of control loops a considerable amount of time will be saved since only data intervals of interest are selected .
  • a method is
  • variance is a simple and fast mathematical operation with no need for extremely powerful data systems. Further, if a large number of data samples are loaded, a lot of time is saved if simple calculations may be used in a first place. A sufficient result of such calculation is a strong indication that such data show sufficient excitation to be useful.
  • the variance of output data in the selected interval is calculated, whereby excitation of data is indicated if the variance exceeds a predetermined threshold value.
  • a selected data interval needs to comprise exciting process output data to be useful. If no excitation of the process output data is indicated for the data interval selected, the process response in the interval is too poor.
  • calculation of variances are fast and simple and thus provide a time saving method to select or to discard a data interval.
  • a method is disclosed where, in the step of selecting, an off-set value is subtracted from the exciting input data, and data where the result of the subtraction differs from zero is selected.
  • a method is disclosed where, in the step of selecting, a data interval where the process is controlled in manual operating mode is selected.
  • a method is disclosed where, in the step of selecting, a data interval where the process is controlled in automatic operating mode is
  • a method is disclosed where, in the step of loading, the input data are filtered, whereby high frequencies are filtered out.
  • a method is disclosed where, in the step of loading, the input data is filtered whereby an approximate time delay of the input data is obtained .
  • a method is disclosed where, the input data is filtered through a cascaded series of filters, whereby the obtained time delay is dependent on the number of filters.
  • Such statistical test may for example by a chi-square test.
  • a method comprising the further steps of estimating a process model with parameters obtained from the input data and output data within the selected data interval, and controlling the industrial process using the estimated process model.
  • a system for controlling or monitoring an industrial process by use of a process model, the system comprising means for loading data from sampled signals logged during performance of the industrial process and stored in a database, means for selecting at least one data interval with exciting input data for
  • a system comprising a processing unit in a computer based system, the processing unit having an internal memory with a computer program product loaded therein,
  • a computer program product on a data carrier comprising computer program code configured to perform the method steps of any of claims 1- 14 is provided, when the program code is loaded into a computer, a controller or terminal connected to a process control system.
  • Fig. 1 shows a block diagram for an industrial process.
  • Fig. 2 shows a flow chart for a prior art method for selecting data intervals.
  • Fig. 3 shows a flow chart for the method according to the present invention.
  • Fig. 4 shows an example of a specific operational situation where the present method is applied.
  • Fig. 1 the most common form of closed loop control of an arbitrary industrial process, Proportional, Integral, Derivative (PID), is shown as a simplified block diagram.
  • the process P(q) for example an industrial plant, is controlled by a controller C(q).
  • a sensor measurement of the process output signal y(t) serves as an input to the controller from a feedback control loop, and any
  • the controller then in turn sends a process input signal u(t) to the process, whereby the sensed value approaches the reference value over time. Changes to the process are effected by that a user changes the setpoint value r(t). In Manual control mode of the controller, the feedback control loop and the controller are disabled and instead the user apply changes to the process by directly changing the process input signal u(t).
  • the process P(q) might also be influenced by noise or environmental disturbances d(t).
  • Fig. 2 shows a flow chart of a known method for selecting data intervals useful for system identification and model estimation.
  • data is loaded from a storage file.
  • the signal indicating operation mode of the controller is decoded to search for manual mode intervals. If an interval in manual mode is found, then this interval is completely scanned for steps in the process input signal with a size above a predefined threshold value. Afterwards it is checked if some steps could be merged to a sequence of steps when they are close to each other.
  • the next step is to search for data samples where the manual input signal and the process output signal were in steady state both before and after the step occurred.
  • the reason for looking after steady state conditions before and after the step is to avoid possible disturbances. Therefore, additionally the time durations where the input signal and the process output signal were both simultaneously in steady state before and after the step are determined. These time durations have to exceed a given threshold value in order for the
  • a last check is performed checking if the time delay of the process response to the step is no longer than a maximal permitted time delay. If the time delay is not too long, the stretch from the beginning of the step of the input signal to the sample where the process output signal returns into steady state is marked as useful for process identification .
  • Fig. 3 shows a flow chart of the present method.
  • a first step is to load data from a database.
  • data samples of relevant signals related to the industrial process are stored during process operation.
  • the exemplified method described below selects data intervals of interest logged during Manual mode operation.
  • Manual mode operation of the controller data intervals with samples of the process input signal u(k) and the process output signal y(k) are used.
  • the method is as well applicable for time periods where the process control has been in Automatic mode. Instead of using samples of the process input signal u(k), data intervals with samples of the setpoint r(k) will in that case be used.
  • the data is as a next step after loading scaled and presented as values between 0% - 100% instead of actual measured values. This is done for practical reasons. By using relative magnitudes, a threshold value set for example to 10% of a maximum input signal need not to be changed if it is to be applied for another process situation, with a different maximum value of the input signal. However, although used in practice, scaling has no relevance per se for the result of the present method .
  • a search for data intervals in the loaded data where the controller mode was set to Manual is performed. Time periods where the controller has been in Manual mode are easily detected, and since a switch to Manual mode per se indicates a likely upcoming change in the signals there is a fair chance of finding useful data intervals.
  • a sample u(k0) is searched for where the difference of the process input signal u(k) and the initial sampled values for the first time differs from zero. Such operation avoids unnecessary further computations.
  • the loaded data of the input signal u(k) are thereafter filtered through a cascade of Laguerre filters. Too fast changes of the process input signal u(k) will, due to the low-pass filtering characteristics of the process response, not result in any detectable change in the process output signal y(k) and will therefore not be useful for process identification or modeling. Therefore, data intervals with high frequency changes of the signals are not desirable. This is one reason for applying a Laguerre model consisting of several cascaded filters from which the first one is a low-pass filter, filtering high frequencies. Another reason for using Laguerre models is that, more or less, all other types of models require prior knowledge (or separate estimation of) the process time delay. It is to be noted that any other suitable basis functions which filter unwanted high frequency signals and inherently model the time delay might be applied. For example if the identified process may have an oscillatory response, so-called Kautz filters may be an
  • the process input signal u(k) is in the next step integrated (not shown in figure) .
  • the order n of Laguerre filters depends on the maximum time delay the Laguerre model should be able to model.
  • a criterion to determine excitation is that the variances should exceed respective threshold.
  • threshold value could for example be a value that is more than three times the estimated noise variance. If the variance relating to the input signal and the variance relating to the output signal does exceed the respective threshold value, this is an indication that more computationally extensive tests for sufficient
  • the calculated Laguerre filter outputs Li(k) . . .L n (k) will form a regression vector ⁇ (if) for each sample point which are then used to form the
  • each row of the regression matrix contains the filter outputs from the first filter Li to the last filter L n for respective sample point from the first sample point ko to the last sample point k N
  • each column of the regression matrix relates to the filter output for sample point k 0 to k N from respective filter Li, L 2 , ...LN- (*o) L i( k o) ⁇ L n ⁇ k 0 )
  • the information matrix is defined as:
  • R is initialized as zero matrix for the first sample.
  • the parameter A* is, for example, taken as 0.95. Afterwards the variances for the filtered input LI and the output y(k) are compared to respective threshold values to check for their magnitude .
  • condition number of the information matrix R(k) is defined as the ratio between its largest and its lowest singular value.
  • the condition number is then compared to a predetermined threshold value. If the condition number is below the threshold value, excitation is indicated and the data in the stretch k est to k is considered as exciting enough.
  • condition number is similar to checking the rank of the matrix.
  • the condition number of the matrix defines the solution accuracy of a linear equation system. If the condition number is low this means that, in a numerical sense, the least- squares problem is well-conditioned. The solution of the
  • equation system is then less sensitive to small variations of the matrix due to the significance of the data. This indicates in turn a certain excitation of the input signal u(k) .
  • Excitation in the input signal u(k) is thus directly indicated by the condition number of the matrix. Further, detection of excitation in the input signal u(k) by computing the condition number is from reality tests known to be a reliable indication of excitation.
  • condition number has its minimum soon after each step occurrence, when the last filter output L n (k) starts to rise/fall. From the sample point where L n (k) returns to steady-state, the condition number increases steeply.
  • the exemplified method searches for excitation of the input and output signals.
  • the use of Laguerre models is an efficient way to handle problems with time delays, and to filter out high frequency disturbances. Furthermore, high frequent changes of the signals are filtered out. Also, problems due to time delays in the process response are taken care of by choosing an
  • condition number of R(k) not only indicates sufficient excitation of data but also gives an indication if the data is suitable for parameter estimation and additionally how reliable and accurate a model can- be estimated.
  • the correlation between the process input and the process output is achieved by performing a chi- square test of the estimated model parameters. Hence, before executing the test, the parameters of the Laguerre model are estimated. This is done by taking the data interval from k est to the current sample k and estimating the parameters.
  • the estimated parameter vector for the Laguerre model is the estimated parameter vector for the Laguerre model.
  • test quantity may, for example, form the test quantity
  • the chi-square test quality is calculated.
  • the estimated parameters are considered as statistically significant enough, that is, the model is
  • the present method also verifies whether there is correlation between the process input and output signals.
  • Laguerre filter outputs are calculated for the setpoint signal r(k) instead of the input signal u(k) .
  • a closed loop system is normally stable. Therefore, an estimation from r(k) to y(k) is possible and requires no separation between processes with or without an integrator, and the regression matrix is already given through the excitation tests.
  • the present method enables selecting useful intervals both from time periods with the controller in automatic mode as well as in manual mode.
  • the chi-square test may be performed as well with the estimated parameters of any other suitable model structure .
  • the method also enables to scan data in automatic mode, even if it requires more computation due to recalculation of the
  • the present method provides detection of excitation as well by the analysis of the condition number and the variances of the sampled input and output signals.
  • a Laguerre model is used.
  • the Laguerre model filters out high frequency disturbances and a parameter estimation is possible without knowing the time delay of the process response in advance.
  • the Laguerre model is linear in the
  • any other suitable filter may be used to filter out high frequencies. Further, problems related to unknown time delays may be dealt with by other means.
  • Laguerre filters any ortho-normal basic function like for example a Kautz filter may be used.
  • the stretch is saved together with an indicator of its quality. Any available measurement signals for the process can be used and the required prior knowledge about the process is kept at a minimum.
  • Fig. 4 plots of relevant signals are shown when a process is exposed to changes in the process input signal u(k) .
  • the process output signal y(k) responds to change of the input signal after a time delay.
  • the input signal u(k) is filtered through a cascade of Laguerre filters. In the example six cascaded filters are used and the respective filter outputs Li, L2, L 3 , L 4 , L5 and h are plotted.
  • the time delay of the filtered signals is seen in the figure for each subsequent filter output plot.
  • a plot of the calculated condition number of the matrix R(k) is shown. Last, the calculated
  • variances of the input signal u(k) and of the output signal y(k) are shown.
  • a time interval of interest is where a change to the input signal causes a process output response.
  • the condition number falls steeply after a step change of the input signal, and the variances at the same time
  • the method will thus indicate 1 and 2 as intervals of interest .
  • the computer program comprises computer program code elements or software code portions that make the computer or processor perform the method using equations, algorithms, data and
  • a part of the program may be stored in a processor as above, but also in a ROM, RAM, PROM or EPROM chip or similar memory storage device.
  • the program in part or in whole may also be stored on, or in, other suitable computer readable medium such as a magnetic disk, CD-ROM or DVD disk, hard disk, magneto-optical memory storage means, in volatile memory, in flash memory, as firmware, or stored on a data server.
  • the program in part or in whole may also be stored on, or in, removable memory media such as a USB memory stick, flash drive, or similar.

Abstract

La présente invention porte sur un procédé de commande ou de surveillance d'un processus industriel par utilisation d'un modèle de processus. Le procédé comprend les étapes consistant à charger des données à partir de données stockées dans une base de données durant la réalisation du processus industriel, et à sélectionner un intervalle de données d'entrée pour estimer un modèle de processus. Le procédé comprend en outre l'étape consistant à vérifier si des données d'entrée dans l'intervalle de données sélectionné influencent ou non le processus. Selon un autre aspect, la présente invention porte sur un procédé de sélection d'un intervalle de données pour identifier un processus industriel à partir d'une base de données comprenant des données journalisées durant le fonctionnement d'un processus industriel. Selon encore d'autres aspects, l'invention porte sur un système réalisant les procédés et sur un système comportant une unité de traitement dans un système informatisé et un produit programme d'ordinateur.
PCT/EP2010/065242 2010-10-12 2010-10-12 Procédé de commande d'un processus industriel WO2012048734A1 (fr)

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JP2015082843A (ja) * 2013-10-24 2015-04-27 ゼネラル・エレクトリック・カンパニイ データストリームにおける不良データを検出、訂正、および検査するためのシステムおよび方法
US10385799B2 (en) 2015-12-30 2019-08-20 International Business Machines Corporation Waveform analytics for optimizing performance of a machine
EP3904977A1 (fr) * 2020-04-30 2021-11-03 ABB Schweiz AG Méthode et système d'assistance aux opérateurs utilisant la méthode

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Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2015082843A (ja) * 2013-10-24 2015-04-27 ゼネラル・エレクトリック・カンパニイ データストリームにおける不良データを検出、訂正、および検査するためのシステムおよび方法
US10385799B2 (en) 2015-12-30 2019-08-20 International Business Machines Corporation Waveform analytics for optimizing performance of a machine
DE102016221808B4 (de) 2015-12-30 2022-08-18 International Business Machines Corporation Signalverlaufsanalyse zum Optimieren einer Leistung einer Maschine
EP3904977A1 (fr) * 2020-04-30 2021-11-03 ABB Schweiz AG Méthode et système d'assistance aux opérateurs utilisant la méthode
WO2021219429A1 (fr) * 2020-04-30 2021-11-04 Abb Schweiz Ag Procédé de création de modèle de traitement

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