CN103970124A - On-line detection method for industrial control loop multi-period oscillation - Google Patents
On-line detection method for industrial control loop multi-period oscillation Download PDFInfo
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Abstract
The invention discloses an on-line detection method for industrial control loop multi-period oscillation. The method includes the following steps that in a control loop to be detected, a set of progress data are collected in real time in an on-line mode; improved intrinsic time-scale decomposition is performed on the progress data in real time in an on-line mode, and monitoring statistics corresponding to all obtained decomposition sub-signals are calculated in real time; whether all the monitoring statistics exceed a set threshold value omega or not is judged, and an on-line detection result is obtained according to all judgment results. By the utilization of the on-line detection method, multi-period oscillation behaviors of the industrial control loop can be detected in a quantified mode to obtain regularity degrees and periods of all oscillation components in the multi-period oscillation. Rich data supports are provided for evaluating the oscillation behaviors and diagnosing a fault source.
Description
Technical field
The present invention relates to the Performance Evaluation field in industrial control system, be specifically related to the online test method of a kind of Industry Control loop multicycle vibration.
Background technology
Modern industry flow sheet equipment has that scale is large, complexity is high, variable is many, and the feature of moving under closed-loop control, for complicated chemical process, often has thousands of loops, and these loops are owing to existing coupling to interact.The oscillatory occurences of control loop due to controller cross adjust, the ubiquity of external disturbance and variable valve nonlinear operation characteristic, greatly affected economic benefit and the stability of the operation of industrial flow equipment.
Industrial flow equipment is carried out to oscillation test tentatively accurately and can reduce the off-time, increase the security of industrial flow equipment operation, reduce manufacturing cost simultaneously.Many controllers can also keep good performance at initial operating stage, but often through after a period of time, owing to being subject to the impact of external environment condition or plant issue, controller performance can decline gradually.Be embodied in control loop process multicycle vibration occurs, the safe and stable operation of industrial process is threatened.Meanwhile, because load and operating mode often change, most of industrial process all shows the characteristic of Non-stationary Data, and the local mean value that is embodied in process data changes.For important control loop, find that in time its oscillating characteristic contributes to engineering staff as early as possible to diagnosing malfunction.Therefore, in industrial control system Performance Evaluation process, timely by on-line monitoring means, effectively detect the multiple vibration of non-stationary process data in control loop, and distinguish different oscillation frequency, significant for controller performance assessment and fault diagnosis.
In prior art, for the oscillation test technology of control loop, the overwhelming majority is to be all applicable to stationary process data, and needs off-line to carry out.Some oscillation test technology for non-stationary process data are there are in recent years.Its main thought has three kinds: based on the time-domain statistical analysis of process data; Autocorrelation function territory ACF based on process data analyzes; Signal decomposition method (comprising empirical mode decomposition and base conversion decomposition) based on process data.There are 3 limitation in the detection method based on Time-domain Statistics and autocorrelation function domain analysis: one in actual applications, the method need to treat testing process have necessarily understand in advance and empirical parameter definite, they are two years old, to Non-stationary Data and cannot realize many oscillation period full-automatic without intervene detect, need targetedly pre-designed wave filter carry out data tranquilization processing with vibration separate, its three, most detection algorithms cannot quantitatively calculate the regular degree of vibration.The detection method of oscillations of the signal decomposition based on process data and upper class detection method exist progressive at present, but limitation is mainly: the subsignal number redundancy that existing signal decomposition technology obtains is various, many subsignals lack physical significance support, do not there is good representativeness, the degree of fitting of the trend to non-stationary signal is also poor, and computation complexity is also higher.In addition, in existing multicycle oscillation test technology, mostly require method off-line to carry out.Minority can realize the online test method for multicycle vibration, its essence is and adopts data window batch processed, and the data of batch processed are shorter, approximate realization detect timely.But its length of window has restricted the assessment degree of accuracy of multicycle vibration greatly, and too short window cannot detect slower oscillation frequency, and long window has been sacrificed again the promptness detecting.
In the practical application of process oscillation test algorithm, detect Industry Control loop and whether there is oscillation behavior, and the rule degree index of qualitative assessment oscillation behavior, generally be applicable to exist the process data of multicycle vibration and non-stationary, and the lot data that can not rely on window realizes online detection, existence for the vibration of Accurate Diagnosis industrial process has very important Practical significance, is also conducive to the control performance qualitative assessment of industrial process.
Summary of the invention
The invention provides a kind of Industry Control loop multicycle vibration online test method, can be applicable to exist the Industry Control circuit process of multicycle oscillation behavior, detection method can on-line implement, generally be applicable to non-stationary or process data stably, only need obtain online conventional operation data, without process mechanism knowledge, decompose by data to be tested being carried out in real time to improved essential time scale, thereby realize the on-line monitoring qualitative assessment to this industrial process multicycle oscillation behavior, can improve accuracy in detection and the reliability of multicycle oscillation behavior, aspect increasing economic efficiency, there is important practical value.
An online test method for Industry Control loop multicycle oscillation behavior, comprises the steps:
In control loop to be detected, one group of process data of online real time collecting;
In real time process data is carried out to improved essential time scale online and decompose, and each decomposes the corresponding monitoring statistic of subsignal to calculate in real time gained;
Whether each monitoring statistic of real-time judge exceedes the threshold value Ω of setting, and comprehensive all judged results obtain online testing result.
The present invention directly adopts the measurable variable of chemical process as process data, and these data obtain by field real-time acquisition, and along with passage of time, constantly gather and renewal process data to supervisory system.First adopt improved essential time scale to decompose, obtain decomposing subsignal set { x
i, this decomposition can be carried out in real time along with the process data of constantly updating in supervisory system, needn't window or batch change and to process; Then calculate each decomposition subsignal x
icorresponding monitoring statistic
the computation complexity of this statistic is minimum, also can carry out in real time large batch of multi-group data simultaneously.Finally, by defined threshold, Ω judges, as a certain decomposition subsignal x
icorresponding monitoring statistic
while exceeding this threshold value, illustrate that this subsignal and original signal vibrate.
The method of online real time collecting process data is in default each sampling interval, record the process data in control loop to be detected, and the process data collecting in each sampling interval to be added on the process data end previously gathering.
Sampling interval refers to the sampling interval of performance evaluation system.Process data x is along with passage of time is constantly updated, and every time span through a sampling interval, all has new process data to add the end of the process data of previous collection to.The sampling interval of performance evaluation system is general identical with the control cycle in industrial control system, also can be chosen as the integral multiple of control cycle, specifically determines according to the requirement of real-time of performance monitoring and industry spot and memory data output restriction.
Wherein, improved essential time scale decomposition method refers to, the index of oscillation I<0.7 that the condition that stops decomposing in the time that essential time scale is decomposed is residual components.
Improved essential time scale is decomposed, on the essential time scale decomposition base of original improvement, improve, former methodical all mathematics and calculated characteristics are retained, just on end condition, simplify and revise, improved decomposition method is for same process data, than former method, the subsignal quantity of acquisition still less, is more suitable in analyzing original signal oscillation behavior.This decomposition computation complexity is very low, and online carrying out that therefore can be real-time, completes calculating in each sampling interval, decomposes subsignal arrangement set { x
i.Retain original subsignal structure and extracting method constant, according to prior art " Frei MG, Osorio I.Intrinsic time-scale decomposition:time – frequency – energyanalysis and real-time filtering of non-stationary signals[J] .Proceedings of theRoyal Society A:Mathematical, Physical and Engineering Science, 2007, 463 (2078): 321-342. " implementing essential time scale decomposes, and the condition that its original method is stopped to decompose is revised as the index of oscillation I<0.7 of residual components.
Index of oscillation, according to prior art " An autonomous valve stiction detectionsystem based on data characterization.Zakharov, A.; Zattoni, E.; Xie, L.; Garcia, O.P.; Jamsa-Jounela, S.L.Control Engineering Practice vol.21issue11November, 2013.p.1507-1518 " obtain.
Improved essential time scale decomposition real-time online carries out, and is interpreted as the each sampling interval at performance evaluation system, all completes the process data x of control loop to be detected is carried out to improved essential time scale decomposition.Be the decomposition subsignal arrangement set { x of process data x
ithat every time span through a sampling interval, all has new decomposition subsignal data to add the end of former decomposition subsignal data to along with passage of time is constantly updated.Because the computation complexity of this detection method is minimum, the sampling interval of performance evaluation system can meet the requirement of computing time completely, and span was from 1 second to 1 minute.
For each decomposition subsignal, monitoring normalized set method specifically comprises the steps:
Step 3-1, obtains zero of each decomposition subsignal and passes through an intervening sequence, decomposes subsignal x for k
k, zero to pass through an intervening sequence be T for it
k;
Step 3-2, calculates zero and passes through an intervening sequence T
kmedian
Step 3-3, calculates zero and passes through an intervening sequence T
krobustness variance
Step 3-4, according to median
with robustness variance
calculate monitoring statistic
In step 3-1, zero passes through an intervening sequence refers to, the intervening sequence between this decomposition subsignal and the intersection point of time shaft, i.e. intervening sequence between the sign symbol reversion position of this decomposition subsignal.Zero each value of passing through in an intervening sequence is arranged from big to small, and the number in the middle of choosing is as median.
In step 3-3, utilize Q
nalgorithm for estimating calculates robustness variance
Use this algorithm estimation variance to there is better robustness.
In step 3-4, monitoring statistic
computing formula as follows:
Wherein, N is the data length of this decomposition subsignal,
it is degree of confidence card side's distribution critical value that degree of freedom is N-1 while being 1-α.
The acquisition of can tabling look-up of the side's of card distribution critical value.Data length N, along with passage of time, along with subsignal Data Update, constantly increases.Sequence T
kmedian
with robustness variance
also along with decomposing subsignal x
kreal-time update and constantly change, therefore monitor statistic
along with the time is constantly updated.
The concrete mode that obtains online testing result according to judged result is: if one of them monitoring statistic
exceed threshold value Ω, judge the decomposition subsignal x that this control loop is corresponding
kthere is vibration, if there are multiple decomposition subsignals to have oscillation behavior in the process data gathering, judge that this control loop exists multicycle oscillation behavior.
Described threshold value Ω is 3.
Threshold value is excessive, detection sensitivity deficiency, and threshold value is too small, easily mid-frequency noise component is mistaken for to oscillating component, as preferably, threshold value is made as to 3.
When
x is described
kin there is oscillation behavior.
The beneficial effect that the present invention compared with prior art has:
1,, without external signal excitation, to the not additional disturbance of system, can realize non-intrusion type completely and detect and diagnosis.
2, calculate simply, convenient operation, without complicated algorithm, is easy to implement on existing DCS workstation or control system host computer.
3, utilize improved essential time scale to decompose the automatic separation that has realized non-stationary component, than prior art, decomposition efficiency is higher, and computation complexity is lower, can meet completely and in a performance evaluation system sampling interval, carry out the requirement of calculating in real time.
4, can carry out quantizating index detection to the multicycle oscillation behavior in Industry Control loop, for evaluation and the source of trouble diagnosis of oscillation behavior provide abundant Data support.
5, adopt the method for data-driven completely, without possessing process mechanism and dynamic perfromance reasoning, also do not need to carry out manual intervention.
Brief description of the drawings
Fig. 1 is the schematic flow sheet of the chemical process in the current embodiment of the present invention;
Fig. 2 is the process data of one group of furnace temp control loop of Real-time Collection in the current embodiment of the present invention;
Fig. 3 decomposes subsignal arrangement set { x in the current embodiment of the present invention
i;
Fig. 4 decomposes the monitoring statistic that subsignal is corresponding in the current embodiment of the present invention
and threshold value Ω position, wherein do not illustrate residual components;
Fig. 5 is method flow diagram of the present invention.
Embodiment
Be example for the Performance Evaluation of main heating furnace in domestic certain large petrochemical plant delayed coking production run below, exist the multicycle oscillation behavior detection method of the chemical process of operation valve viscosity property to be described in detail the present invention.
As shown in Figure 1, petrochemical process heating furnace is the important step and one of main energy consumption unit in production procedure, and the steady control of heater outlet temperature is for improving product quality and reducing energy consumption important in inhibiting.
Heating furnace is supplied heat-obtaining by device in Gas, gas amount changes and fluctuates according to upstream oiliness, need to control air intake and make device in Gas fully burn to obtain maximum heat, should ensure certain air surplus, but too much Cryogenic air can be taken away furnace heat simultaneously, cause waste of fuel, loss economic benefit, therefore, using furnace outlet temperature as controlled variable, fuel device in Gas aperture is carried out circuit controls as performance variable, and process exists random perturbation simultaneously.
Device in Gas degree adjustment valve (operation valve) belongs to the topworks of this control loop, and operation occurs certain nonlinear characteristic after a period of time, and because controller is crossed the reason such as adjust, multicycle oscillation behavior easily appears in control loop.External disturbance is introduced this loop by coupling circuit, easily causes the vibration of loop multicycle.The process data that the current embodiment of the present invention gathers is furnace outlet temperature data.Furnace outlet temperature data after standardization as shown in Figure 2, in Fig. 2, horizontal ordinate is sampled point ordinal number, unit is the sampling interval of the corresponding data of Samples(1 Sample), ordinate is furnace outlet temperature under the nominal situation after standardization, unit is DEG C.
As shown in Figure 5, specific embodiment of the invention is as follows:
In control loop to be detected, real-time online gathers one group of process data x, as shown in Figure 2.
To the process data x collecting
t, to carry out improved essential time scale real-time online and decompose, the condition that wherein stops decomposing is, the index of oscillation I<0.7 of residual components.In decomposable process, calculate residual components and find, the index of oscillation I=0.13 of a certain residual components, now stops decomposing.Residual components is now corresponding to subsignal x
4.The decomposition subsignal arrangement set { x that the essential time scale that is improved is decomposed
1, x
2, x
3, x
4, as shown in Figure 3.
For decomposing subsignal arrangement set { x
1, x
2, x
3, x
4in each decompose subsignal sequence, calculate in real time corresponding monitoring statistic, gathered
account form is as follows:
Step 3-1, obtains zero of each decomposition subsignal and passes through an intervening sequence, decomposes subsignal x for k
k, zero to pass through an intervening sequence be T for it
k;
Step 3-2, calculates zero and passes through an intervening sequence T
kmedian
Step 3-3, utilizes Q
nalgorithm for estimating calculates zero and passes through an intervening sequence T
krobustness variance
Step 3-4, according to median
with robustness variance
calculate monitoring statistic
Monitoring statistic
computing formula as follows:
Wherein, N is the data length of this decomposition subsignal,
it is degree of confidence card side's distribution critical value that degree of freedom is N-1 while being 1-α.Degree of confidence 1-α gets 0.95 in embodiments of the present invention, and corresponding parameter alpha is 0.05, monitoring statistic
in the real-time result of calculation of each sampling interval as shown in Figure 4,
corresponding residual components subsignal x
4monitoring parameter, be always 0, in the time that reality is implemented, can calculate and map for easy.
As can be seen from Figure 4, monitoring statistic
with
exceed defined threshold Ω=3, threshold value is shown in dotted line, and this loop corresponding subsignal component x is described
1and x
3vibrate, thereby confirm that this furnace outlet temperature data exists the oscillation behavior of two different cycles.
Utilize the inventive method, can quantitatively detect the multicycle oscillation behavior in Industry Control loop, obtaining the multicycle vibrates regular degree and the cycle of each oscillating component.For evaluation and the source of trouble diagnosis of oscillation behavior provide abundant Data support.
Claims (8)
1. an online test method for Industry Control loop multicycle oscillation behavior, is characterized in that, comprises the steps:
In control loop to be detected, one group of process data of online real time collecting;
In real time process data is carried out to improved essential time scale online and decompose, and each decomposes the corresponding monitoring statistic of subsignal to calculate in real time gained;
Whether each monitoring statistic of real-time judge exceedes the threshold value Ω of setting, and comprehensive all judged results obtain online testing result.
2. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, it is characterized in that, the method of online real time collecting process data is, in default each sampling interval, record the process data in control loop to be detected, and the process data collecting in each sampling interval is added on the process data end previously gathering.
3. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, it is characterized in that, wherein, improved essential time scale decomposition method refers to, the index of oscillation I<0.7 that the condition that stops decomposing in the time that essential time scale is decomposed is residual components.
4. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, is characterized in that, for each decomposition subsignal, monitoring normalized set method specifically comprises the steps:
Step 3-1, obtains zero of each decomposition subsignal and passes through an intervening sequence, decomposes subsignal x for k
k, zero to pass through an intervening sequence be T for it
k;
Step 3-2, calculates zero and passes through an intervening sequence T
kmedian
Step 3-3, calculates zero and passes through an intervening sequence T
krobustness variance
Step 3-4, according to median
with robustness variance
calculate k and decompose subsignal x
kcorresponding monitoring statistic
5. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, is characterized in that, in step 3-3, utilizes Q
nalgorithm for estimating calculates robustness variance
6. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, is characterized in that, in step 3-4, and monitoring statistic
computing formula as follows:
Wherein, N is the data length of this decomposition subsignal,
it is degree of confidence card side's distribution critical value that degree of freedom is N-1 while being 1-α.
7. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, is characterized in that, the concrete mode that obtains online testing result according to judged result is: if one of them monitoring statistic
exceed threshold value Ω, judge the decomposition subsignal x that this control loop is corresponding
kthere is vibration, if there are multiple decomposition subsignals to have oscillation behavior in the process data gathering, judge that this control loop exists multicycle oscillation behavior.
8. the online test method of Industry Control loop multicycle oscillation behavior as claimed in claim 1, is characterized in that, described threshold value Ω is 3.
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104950873A (en) * | 2015-05-29 | 2015-09-30 | 浙江大学 | Method for detecting intermittent oscillation of industrial control circuits in online manner |
| CN105607477A (en) * | 2016-01-20 | 2016-05-25 | 浙江大学 | Industrial control circuit oscillation detection method based on improved local mean value decomposition |
| CN106773693A (en) * | 2016-12-21 | 2017-05-31 | 浙江大学 | A kind of sparse causality analysis method of Industry Control multi-loop oscillation behavior |
| CN107368059A (en) * | 2017-07-21 | 2017-11-21 | 浙江大学 | A kind of industrial process multi-loop oscillation detection method decomposed based on quick multiple dimension essence time scale |
| CN107436598A (en) * | 2017-07-21 | 2017-12-05 | 浙江大学 | The industrial multi-loop oscillation detection method decomposed based on Multidimensional Nature time scale |
| CN105511454B (en) * | 2016-01-20 | 2018-05-22 | 浙江大学 | A kind of process control loops time-varying oscillation behavior detection method |
| CN110687791A (en) * | 2019-10-31 | 2020-01-14 | 浙江大学 | Nonlinear oscillation detection method based on improved adaptive frequency modulation modal decomposition |
| CN111537893A (en) * | 2020-05-27 | 2020-08-14 | 中国科学院上海高等研究院 | Method and system for evaluating operation safety of lithium ion battery module and electronic equipment |
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2014
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN104950873A (en) * | 2015-05-29 | 2015-09-30 | 浙江大学 | Method for detecting intermittent oscillation of industrial control circuits in online manner |
| CN104950873B (en) * | 2015-05-29 | 2017-07-21 | 浙江大学 | The online test method of process control loops intermittent oscillation |
| CN105607477A (en) * | 2016-01-20 | 2016-05-25 | 浙江大学 | Industrial control circuit oscillation detection method based on improved local mean value decomposition |
| CN105607477B (en) * | 2016-01-20 | 2018-05-11 | 浙江大学 | A kind of process control loops detection method of oscillations decomposed based on improvement local mean value |
| CN105511454B (en) * | 2016-01-20 | 2018-05-22 | 浙江大学 | A kind of process control loops time-varying oscillation behavior detection method |
| CN106773693A (en) * | 2016-12-21 | 2017-05-31 | 浙江大学 | A kind of sparse causality analysis method of Industry Control multi-loop oscillation behavior |
| CN107368059A (en) * | 2017-07-21 | 2017-11-21 | 浙江大学 | A kind of industrial process multi-loop oscillation detection method decomposed based on quick multiple dimension essence time scale |
| CN107436598A (en) * | 2017-07-21 | 2017-12-05 | 浙江大学 | The industrial multi-loop oscillation detection method decomposed based on Multidimensional Nature time scale |
| CN107368059B (en) * | 2017-07-21 | 2019-08-30 | 浙江大学 | A kind of industrial process multi-loop oscillation detection method decomposed based on quick multiple dimension essence time scale |
| CN107436598B (en) * | 2017-07-21 | 2019-09-03 | 浙江大学 | The industrial multi-loop oscillation detection method decomposed based on Multidimensional Nature time scale |
| CN110687791A (en) * | 2019-10-31 | 2020-01-14 | 浙江大学 | Nonlinear oscillation detection method based on improved adaptive frequency modulation modal decomposition |
| CN111537893A (en) * | 2020-05-27 | 2020-08-14 | 中国科学院上海高等研究院 | Method and system for evaluating operation safety of lithium ion battery module and electronic equipment |
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