CN110018670B - Industrial process abnormal working condition prediction method based on dynamic association rule mining - Google Patents

Industrial process abnormal working condition prediction method based on dynamic association rule mining Download PDF

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CN110018670B
CN110018670B CN201910244877.8A CN201910244877A CN110018670B CN 110018670 B CN110018670 B CN 110018670B CN 201910244877 A CN201910244877 A CN 201910244877A CN 110018670 B CN110018670 B CN 110018670B
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association rule
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item set
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CN110018670A (en
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徐正国
王豆
陈积明
程鹏
孙优贤
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Zhejiang University ZJU
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Abstract

The invention discloses an industrial process abnormal working condition prediction method based on dynamic association rule mining, which can be applied to fault prediction and health management of an industrial process. The method utilizes a sliding window mode to mine the dynamic association rule of the operation parameters of the industrial equipment and introduces the dynamic association rule into the prediction of the abnormal working conditions in the industrial process. The invention considers the time characteristic of the association rule, limits the data length by using a sliding window, provides an association rule mining algorithm for mining the dynamic association rule of operating parameters pairwise, and then introduces the association rule mining result into wavelet neural network prediction to continuously update the network by the dynamic association rule so as to obtain a more accurate prediction result. The method has great application value for fault prediction and health management in engineering.

Description

Industrial process abnormal working condition prediction method based on dynamic association rule mining
Technical Field
The invention belongs to the technical field of reliability maintenance engineering, and relates to an industrial process abnormal working condition prediction method based on dynamic association rule mining.
Background
With the continuous emergence of complex systems and the increasing demand of real-time monitoring of industrial processes, modern industrial equipment is often equipped with a plurality of sensors to monitor the operation state of the industrial equipment in the operation process. Meanwhile, multiple fault modes may occur in the operation process of the equipment, a certain fault may correspond to a plurality of symptoms, and under the condition, the single sensor information cannot completely reflect the operation state of the equipment, so that fault prediction based on multi-sensor information is generated at the right moment. The failure prediction based on multi-sensor information aims to analyze the operation state of the equipment using comprehensive sensor information, thereby making more reliable equipment diagnosis and prediction. With the continuous development of sensing technology, the use of multiple sensors for condition monitoring, fault diagnosis and prediction of equipment has become a trend.
In the field of fault prediction, the work of combining association rule mining and fault prediction is still fresh at present. Conventional association rule mining algorithms consider that association rules found for an object system are invariant, i.e., valid when analyzing other data sets of the system. The association rule mined in the way does not take the time factor into consideration, so that a static association rule is obtained. In fact, as the data is updated, it may be accompanied by changes in the operating state of the system, and in turn, changes in the characteristics of the data, and thus the association rules. During the operation of the industrial plant, the operating state is not constant. As the data is updated or when the operation state of the equipment changes, the characteristics of the operation parameters of the equipment also change, so that the relevance of the operation parameters may also change. In this case, it is obviously not reasonable to use the association rules mined from the original dataset to guide the subsequent decision.
Disclosure of Invention
Aiming at the current situation of the prior art, the invention aims to solve the problem that dynamic association of sensor data is rarely considered in the existing data-driven prediction technology, provides an equipment abnormal working condition prediction method based on an operation parameter dynamic association rule, and updates a wavelet neural network by using the dynamic association rule and performs abnormal working condition prediction (fault prediction).
The concept of the present invention will now be explained as follows:
the invention introduces dynamic association rule mining aiming at the problem that the association between the operation parameters in the operation process of the industrial equipment is likely to change along with the change of the operation state of the equipment. The invention utilizes a sliding window mode to carry out association rule mining on the data in each window, thereby obtaining the dynamically changed association rule along with the sliding of the window. In order to obtain the complete change condition of the parameter relevance, the method provided by the invention does not set the minimum support degree and confidence degree threshold value during the mining of the association rule of the parameter level, but stores the association rules of all the parameters in pairs and describes the strength of the association rules with the support degree and the confidence degree. And then, introducing the dynamic association rule mining result into the training and updating of the wavelet neural network, and predicting the abnormal working condition through the association parameters obtained by mining the dynamic association rule.
According to the invention concept, the invention provides an industrial process abnormal working condition prediction method based on dynamic association rule mining, which comprises the following specific steps:
step 1: carrying out data preprocessing on a measured value time sequence acquired by a sensor in an industrial process based on a sliding window, and carrying out piecewise linearization representation, clustering and symbolization on the time sequence to generate a transaction set suitable for association rule mining;
step 2: generating a frequent item set by adopting a two-stage frequent item set generation strategy, and mining association rules of every two parameters;
and step 3: performing association rule mining by using an initial data set, and training an initial wavelet neural network model based on an initial association rule mining result;
and 4, step 4: sliding a data window based on data updating, and updating an association rule and a wavelet neural network model for a new data window;
and 5: and predicting abnormal working conditions based on the updated wavelet network model, and continuously updating the association rule and the wavelet neural network model before predicting the occurrence of the abnormal working conditions.
Based on the above scheme, the following implementation manner can be specifically adopted for each step:
preferably, the step 1 is specifically realized by the following sub-steps:
step 1.1: the sensor measurement sequence is recorded as
Figure BDA0002010777970000021
N is the number of sensors, K is the sequence length, K is 1,2, …, K, the length of the sliding window is L, the sliding distance is l each time, and the sliding window is WkThe data contained in the window is
Figure BDA0002010777970000022
Step 1.2: and performing time series piecewise linearization representation, clustering and symbolization processing on each data window, wherein the specific process is as follows:
for each data window WkPerforming the following processes of 1.2.1-1.2.5:
1.2.1. setting the initial fitting starting point to
Figure BDA0002010777970000031
End point is
Figure BDA0002010777970000032
h is 2; fitting error threshold value is omegaE(ii) a Initializing a segmentation point count value 1;
1.2.2. for each fitting starting point in turn
Figure BDA0002010777970000033
Performing step 1.1) -step 1.4):
1.1) first calculating end ═ start + h;
1.2) for data
Figure BDA0002010777970000034
Fitting by using a least square method, and calculating a fitting error ERR;
1.3) if the fitting error ERR is not greater than the fitting error threshold value omegaEIf h is h +1, skipping to step 1) again;
1.4) if the fitting error ERR is larger than the fitting error threshold value omegaEThen ensureSegment storage point
Figure BDA0002010777970000035
Figure BDA0002010777970000036
To Pi(ii) a Saving fitted line segments
Figure BDA0002010777970000037
Representing the fitted line segment by means of a triplet
Figure BDA0002010777970000038
And store to
Figure BDA0002010777970000039
kiWhich represents the slope of the line segment,
Figure BDA00020107779700000310
represents the span of the line segment on the time axis, riRepresenting a growth rate of the line segment data; resetting the fitting starting point start to be start + h and resetting h to be 2; updating count + 1;
1.2.3. circularly executing 1.2.2 until end is larger than k + L-1 to obtain a fitted linear time sequence
Figure BDA00020107779700000311
And segmentation point
Figure BDA00020107779700000312
Composed sequence of segmentation points Pi；
1.2.4. With Wk-1The clustering center of the window is the initial clustering center, and the triple sequence is subjected to K-means clustering algorithm
Figure BDA00020107779700000313
Clustering is carried out, and different symbols are distributed to the line segments of different classes to obtain symbolic sequences
Figure BDA00020107779700000314
Wherein in the clustering processTwo line segments s are described by indexes based on Euclidean distanceiAnd sjSimilarity between them:
Figure BDA00020107779700000315
wherein d isijI.e. representing a line segment siAnd sjSimilarity of (d)ijThe smaller the size, the more similar the change form of the two line segments, ωkAnd ωrRepresenting a weight;
1.2.5. for two operating parameters ViAnd VjRespectively obtaining the symbolized sequences
Figure BDA00020107779700000316
And
Figure BDA00020107779700000317
segment point sequence P combining two operating parametersiAnd PjAnd symbolizing the sequence of the combined segmentation points
Figure BDA0002010777970000041
And
Figure BDA0002010777970000042
performing segmentation reconstruction to obtain reconstructed symbolic sequence
Figure BDA0002010777970000043
And
Figure BDA0002010777970000044
from which a transaction set is formed, nij-1 is PiAnd PjThe number of the combined segmentation points.
Preferably, the step 2 is realized by the following sub-steps:
step 2.1: each transaction of the transaction set obtained by step 1 is marked as
Figure BDA0002010777970000045
In addition will
Figure BDA0002010777970000046
And
Figure BDA0002010777970000047
the line segment type symbols included in (1) are respectively marked as
Figure BDA0002010777970000048
And
Figure BDA0002010777970000049
miand mjAre respectively as
Figure BDA00020107779700000410
And
Figure BDA00020107779700000411
the number of line segment categories contained in (1); setting the minimum support threshold of the frequent item set generation process as min1；
Step 2.2: for an initial window of data W1And (3) performing association rule mining, wherein the specific process is as follows:
2.2.1) scanning the sensor measurement sequence dataset of all sensors to calculate the support of each term, let σ (-) denote the support count of a term or set of terms, initially 0; suppose that
Figure BDA00020107779700000412
Is denoted by the class symbol tkE represents i or j, t represents c or d;
for each one
Figure BDA00020107779700000413
Calculating σ (t)k)＝σ(tk)+1；
For each tkJudgment of
Figure BDA00020107779700000414
If yes, the method determines that t is truekFor frequent 1-item sets, reserve tkAnd recordCorresponding support degree count σ (t)k)；
2.2.2) use the resulting frequent 1-item set tkForm 2-item set and find frequent 2-item set, respectivelypAnd dqRespectively, the symbols of the original line segment class after the step 2.2.1)
Figure BDA00020107779700000415
And
Figure BDA00020107779700000416
the item retained in (1);
for each { cp,dqExecuting the operation: for each one exists in
Figure BDA00020107779700000417
C in (2)p,dq}, calculate σ ({ c)p,dq})＝σ({cp,dq}) + 1; if it is not
Figure BDA00020107779700000418
Not less than min1Then consider { cp,dqKeep { c) for frequent 2-item setp,dqAnd recording corresponding support degree counts;
2.2.3) use the frequent 2-item set { c) obtained in step 2.2.2p,dqEvery two operation parameters V are calculatediAnd VjThe support in the whole data set is calculated as follows: for every two operating parameters ViAnd VjSet of formed items { Vi,Vj}, calculate σ ({ V)i,Vj})＝sum(σ({cp,dq})) two parameters, associated rule support
Figure BDA0002010777970000051
And calculates σ (V)i)＝sum(σ(cp))；σ(Vj)＝ sum(σ(dq))；
2.2.4) for each group { Vi,VjResults in the following association rules: vj→ViAnd Vi→Vj(ii) a For each association rule, calculate its confidence as
Figure BDA0002010777970000052
Step 2.3: for each new data window, the following process is adopted for frequent item set generation and association rule generation:
for each k>1 data window WkPerforming steps 2.3.1) to 2.3.3):
2.3.1) transaction to each sliding-out Window
Figure BDA0002010777970000053
Is marked as { ca1,da2}, judging { ca1,da2Whether the item is a frequent item set or not, if so, updating the sigma ({ c)a1,da2})＝σ({ca1,da2})-1；
2.3.2) transactions for each sliding-in window
Figure BDA0002010777970000054
Is marked as { cb1,db2}, judging { cb1,db2Whether the item set is frequent or not, if yes, update sigma ({ c)b1,db2})＝σ({cb1,db2}) +1, if not, in the data window WkInternally calculating the { cb1,db2Support count of σ ({ c)c1,dc2})；
2.3.3) for each { c }a1,da2Get it before
Figure BDA0002010777970000055
Update { c }a1,da2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cb1,db2Get it before
Figure BDA0002010777970000056
Update { c }b1,db2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cc1,dc2Get it before
Figure BDA0002010777970000057
Update { c }c1,dc2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set;
2.3.4) update the support and confidence of each association rule using steps 2.2.3) and 2.2.4).
Preferably, the step 3 is implemented by the following sub-steps:
the preset prediction step length is recorded as lpThe association parameter extracted from the association rule mined from the training data set is { V }1,V2,…,VuU is the number of the extracted group of associated parameters; for each parameter ViThe measured value sequence is
Figure BDA0002010777970000058
Construct the following matrix ItrainFor initial neural network training inputs:
Figure BDA0002010777970000059
wherein, ItrainEach column in the training input sample is a training input sample; structural training output OtrainIn order to realize the purpose,
Figure BDA0002010777970000061
training the wavelet neural network by using the training sample constructed by the formula; at network initialization, association rule V is utilizedi→VuCorresponding initial confidence ωiAnd setting an initial weight value between the network input layer and the hidden layer, wherein i is 1,2, … u-1.
Preferably, the step 4 is implemented by the following sub-steps:
step 4.1: the updating method of the association rule comprises the following steps: recording c new data collected by the sensor in the running process of the equipment as
Figure BDA0002010777970000062
c>L, using the most recent L data in the newly collected data
Figure BDA0002010777970000063
Update the association rule through step 2.3, thereby updating association rule Vi→VuThe corresponding confidence is recorded as
Figure BDA0002010777970000064
Step 4.2: the model updating method comprises the following steps: combining the training data and the newly acquired data to construct a new model training sample, i.e. a data set of
Figure BDA0002010777970000065
Construction matrix ItestConstructing O for input of neural network updatetestIn order to be output, the output is,
Figure BDA0002010777970000066
Figure BDA0002010777970000067
constructing a new training sample by using the formula, and training the wavelet neural network; at network initialization, the new confidence level is utilized
Figure BDA0002010777970000068
And setting an initial weight value between the network input layer and the hidden layer.
Preferably, the step 5 is implemented by the following sub-steps:
recording a preset abnormal working condition (failure) occurrence threshold value as omegapFor the latest collected data, l is performed by using the model updated in step 4pAnd predicting, namely if the obtained target parameter predicted value exceeds a set threshold relative to the initial normal drift amount, determining that an abnormal working condition occurs.
Preferably, the association rules and models are updated back to step 4 each time a predetermined amount of data is updated before the prediction process is finished.
The method utilizes a sliding window mode to mine the dynamic association rule of the operation parameters of the industrial equipment and introduces the dynamic association rule into the prediction of the abnormal working conditions in the industrial process. The invention considers the time characteristic of the association rule, limits the data length by using a sliding window, provides an association rule mining algorithm for mining the dynamic association rule of operating parameters pairwise, and then introduces the association rule mining result into wavelet neural network prediction to continuously update the network by the dynamic association rule so as to obtain a more accurate prediction result. The method has great application value for fault prediction and health management in engineering.
Drawings
FIG. 1 is a confidence curve of the IDV (13) association rule (v13, v16, v36 → v7) in the embodiment;
FIG. 2 is a confidence curve of the IDV (13) association rule (v35, v36 → v11) in the embodiment;
FIG. 3 shows the predicted result of variable 7 of IDV (13) in the example;
FIG. 4 shows the prediction error of the IDV (13) variable 7 in the example;
FIG. 5 shows the predicted result of the variable 11 of IDV (13) in the example;
FIG. 6 shows the prediction error of IDV (13) variable 11 in an embodiment.
Detailed Description
The embodiments of the present invention will now be further described with reference to the accompanying drawings.
The following embodiment specifically explains the specific operation steps and the effect of the verification method through the tennessee-iseman (TE) process simulation data.
The data set was sampled at 3 minute intervals and recorded the variable measurements taken by each sensor at that sampling interval. Under each operating condition (normal operating state and fault operating state under 21 preset faults), the measurement data of the simulation process will generate two types of data sets, namely a training set and a test set. The acquisition process of the training set is measured values of all 52 variables obtained under the condition that the simulation process runs for 25 hours, wherein, except the training set acquired under the normal running state, the acquisition of the other 21 training set data introduces faults after the simulation process runs for 1 hour, and only the measured data of the following 24 hours are recorded. That is, the training set in the normal operation state has 500 observation samples, and the training sets collected in the remaining 21 fault states are all 480 observation samples. In addition, for 22 test sets, the data is all the variable measurement values collected after the simulation process runs for 48 hours, that is, each test set contains 960 sample data. It should be noted that in the simulation of 21 process faults, the corresponding fault was introduced after the simulation was run for 8 hours. Therefore, for the test set in 21 fault operation states, the first 160 observation samples are normal data, and the last 800 observation samples are fault data. In the TE process simulation model, only IDV (13) is a slowly varying fault, so in this example we use the relevant data of IDV (13) to perform experiments.
The method for predicting the abnormal working condition of the industrial process comprises the following specific implementation processes:
step 1: the method comprises the steps of preprocessing measured value time sequences acquired by sensors in the industrial process based on a sliding window, and performing piecewise linearization representation, clustering and symbolization on the time sequences to generate a transaction set suitable for association rule mining. The step is realized by the following steps:
step 1.1: the sensor measurement sequence is recorded as
Figure BDA0002010777970000081
N is the number of sensors, K is the sequence length, K is 1,2, …, K, the length of the sliding window is L, the sliding distance is l each time, and the sliding window is WkThe data contained in the window is
Figure BDA0002010777970000082
It should be noted that in the present invention, i and j are numbers indicating sensors as superscripts and are numbers indicating only ordinal numbers as subscripts, regardless of the sensor numbers.
Step 1.2: and performing time series piecewise linearization representation, clustering and symbolization processing on each data window, wherein the specific process is as follows:
for each data window WkPerforming the following processes of 1.2.1-1.2.5:
1.2.1. setting the initial fitting starting point to
Figure BDA0002010777970000083
End point is
Figure BDA0002010777970000084
h is 2; fitting error threshold value is omegaE(ii) a Initializing a segmentation point count value 1;
1.2.2. for each fitting starting point in turn
Figure BDA0002010777970000085
Performing step 1.1) -step 1.4):
1.1) first calculating end ═ start + h;
1.2) for data
Figure BDA0002010777970000086
Fitting by using a least square method, and calculating a fitting error ERR;
1.3) if the fitting error ERR is not greater than the fitting error threshold value omegaEIf h is h +1, skipping to step 1) again;
1.4) if the fitting error ERR is larger than the fitting error threshold value omegaEThen save the segmentation point
Figure BDA0002010777970000087
Figure BDA0002010777970000088
To Pi(ii) a Saving fitted line segments
Figure BDA0002010777970000089
Representing the fitted line segment by means of a triplet
Figure BDA00020107779700000810
And store to
Figure BDA00020107779700000811
kiWhich represents the slope of the line segment,
Figure BDA00020107779700000812
represents the span of the line segment on the time axis, riRepresenting a growth rate of the line segment data; resetting the fitting starting point start + h and resetting h 2 (resetting the fitting starting point and h); updating count + 1;
1.2.3. circularly executing 1.2.2 until end that end is larger than k + L-1, and obtaining a line-segment time sequence after least square fitting
Figure BDA0002010777970000091
(with multiple line segments fitted therein) and segmentation points
Figure BDA0002010777970000092
Composed sequence of segmentation points Pi；
1.2.4. With Wk-1The clustering center of the window is the initial clustering center, and the triple sequence is subjected to K-means clustering algorithm
Figure BDA0002010777970000093
Clustering is carried out, and different symbols are distributed to the line segments of different classes to obtain symbolic sequences
Figure BDA0002010777970000094
Wherein, two line segments s are described by indexes based on Euclidean distance in the clustering processiAnd sjSimilarity between them:
Figure BDA0002010777970000095
wherein d isijI.e. representing a line segment siAnd sjSimilarity of (d)ijThe smaller the size, the more similar the change form of the two line segments, ωkAnd ωrRepresenting a weight;
1.2.5. for two operating parameters ViAnd VjRespectively obtaining the symbolized sequences
Figure BDA0002010777970000096
And
Figure BDA0002010777970000097
segment point sequence P combining two operating parametersiAnd PjAnd symbolizing the sequence of the combined segmentation points
Figure BDA0002010777970000098
And
Figure BDA0002010777970000099
performing segmentation reconstruction to obtain reconstructed symbolic sequence
Figure BDA00020107779700000910
And
Figure BDA00020107779700000911
from which a transaction set is formed, nij-1 is PiAnd PjThe number of the combined segmentation points.
Step 2: and generating a frequent item set by adopting a two-stage frequent item set generation strategy, and mining association rules of every two parameters. The step is realized by the following steps:
step 2.1: each transaction of the transaction set obtained by step 1 is marked as
Figure BDA00020107779700000912
In addition will
Figure BDA00020107779700000913
And
Figure BDA00020107779700000914
the line segment type symbols included in (1) are respectively marked as
Figure BDA00020107779700000915
And
Figure BDA00020107779700000916
miand mjAre respectively as
Figure BDA00020107779700000917
And
Figure BDA00020107779700000918
the number of line segment categories contained in (1); setting the minimum support threshold of the frequent item set generation process as min1(ii) a In this example, the minimum support threshold is set to 0.2;
step 2.2: for an initial window of data W1And (3) performing association rule mining, wherein the specific process is as follows:
2.2.1) scanning the sensor measurement sequence dataset of all sensors to calculate the support of each term, let σ (-) denote the support count of a term or set of terms, initially 0; suppose that
Figure BDA00020107779700000919
Is denoted by the class symbol tkE represents i or j, t represents c or d (i.e. c)kOr dk)；
For each one
Figure BDA0002010777970000101
Update calculation σ (t)k)＝σ(tk)+1；
For each tkJudgment of
Figure BDA0002010777970000102
If yes, the method determines that t is truekFor frequent 1-item sets, reserve tkAnd records the corresponding support count σ (t)k)；
2.2.2) use the resulting frequent 1-item set tkForm 2-item set and find frequent 2-item set, respectivelypAnd dqRespectively, the symbols of the original line segment class after the step 2.2.1)
Figure BDA0002010777970000103
And
Figure BDA0002010777970000104
the item retained in (1);
for each { cp,dqExecuting the operation: for each one exists in
Figure BDA0002010777970000105
C in (2)p,dq}, calculate σ ({ c)p,dq})＝σ({cp,dq}) + 1; if it is not
Figure BDA0002010777970000106
Not less than min1Then consider { cp,dqKeep { c) for frequent 2-item setp,dqAnd recording corresponding support degree counts;
2.2.3) use the frequent 2-item set { c) obtained in step 2.2.2p,dqEvery two operation parameters V are calculatediAnd VjThe support in the whole data set is calculated as follows: for every two operating parameters ViAnd VjSet of formed items { Vi,Vj}, calculate σ ({ V)i,Vj})＝sum(σ({cp,dq})) two parameters, associated rule support
Figure BDA0002010777970000107
And calculates σ (V)i)＝sum(σ(cp))；σ(Vj)＝ sum(σ(dq))；
2.2.4) for each group { Vi,VjResults in the following association rules: vj→ViAnd Vi→Vj(ii) a For each association rule, calculate its confidence as
Figure BDA0002010777970000108
In this example, for IDV (13), the initial association rules are mined through a training set. The support threshold is set to 0.2, and the confidence threshold is set to 0.7, so as to obtain several different sets of association rules, the results of which can be seen in table 1, wherein two sets of association parameters are included, namely (variable 13, variable 16, variable 36 → variable 7) and (variable 35, variable 36 → variable 11).
Step 2.3: for each new data window, the following process is used to perform frequent item set generation and generate association rules. In this example, the length of the sliding window for dynamic association rule mining is set to 100, and the sliding window is set to 1 data point at a time.
For each k>1 data window WkPerforming steps 2.3.1) to 2.3.3):
2.3.1) transaction to each sliding-out Window
Figure BDA0002010777970000109
Is marked as { ca1,da2}, judging { ca1,da2Whether the item is a frequent item set or not, if so, updating the sigma ({ c)a1,da2})＝σ({ca1,da2})-1；
2.3.2) transactions for each sliding-in window
Figure BDA0002010777970000111
Is marked as { cb1,db2}, judging { cb1,db2Whether the item set is frequent or not, if yes, update sigma ({ c)b1,db2})＝σ({cb1,db2}) +1, if not, in the data window WkInternally calculating the { cb1,db2Support count of σ ({ c)c1,dc2})；
2.3.3) for each { c }a1,da2Get it before
Figure BDA0002010777970000112
Update { c }a1,da2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cb1,db2Get it before
Figure BDA0002010777970000113
Update { c }b1,db2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cc1,dc2Get it before
Figure BDA0002010777970000114
Update { c }c1,dc2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set;
2.3.4) update the support and confidence of each association rule using steps 2.2.3) and 2.2.4).
With the test set of IDVs (13), starting from the 100 th point, every time 1 sample of data is updated, the first 100 points including this point are used to mine the association rule, and the resulting confidence change situation is shown in fig. 1 and 2. As can be seen, the association rules (confidence) change as the data is updated, but the values for both sets of parameters of IDV (13) are maintained at a higher level.
And step 3: and (5) performing association rule mining by using the initial data set, and training an initial wavelet neural network model based on the initial association rule mining result. The step is realized by the following steps:
the preset prediction step length is recorded as lpIn this case 10. The association parameter extracted from the association rule mined from the training data set is set as V1,V2,…,VuU is the number of the extracted group of associated parameters; for each parameter ViThe measured value sequence is
Figure BDA0002010777970000115
Construct the following matrix ItrainFor initial neural network training inputs:
Figure BDA0002010777970000116
wherein, ItrainEach column in the training input sample is a training input sample; structural training output OtrainIn order to realize the purpose,
Figure BDA0002010777970000117
training the wavelet neural network by using the training sample constructed by the formula; at network initialization, association rule V is utilizedi→VuCorresponding initial confidence ωiAnd setting an initial weight value between the network input layer and the hidden layer, wherein i is 1,2, … u-1.
And 4, step 4: and sliding a data window based on data updating, and updating an association rule and a wavelet neural network model for a new data window. The step is realized by the following steps:
step 4.1: the updating method of the association rule comprises the following steps: recording c new data collected by the sensor in the running process of the equipment as
Figure BDA0002010777970000121
c>L, using the most recent L data in the newly collected data
Figure BDA0002010777970000122
Update the association rule through step 2.3, thereby updating association rule Vi→VuThe corresponding confidence is recorded as
Figure BDA0002010777970000123
Note that c is greater than the preset sliding window length L, that is, the update of the association rule is only started when the device is running and the length of the collected data is greater than L.
Step 4.2: the model updating method comprises the following steps: combining the training data and the newly acquired data to construct a new model training sample, i.e. a data set of
Figure BDA0002010777970000124
Construction matrix ItestConstructing O for input of neural network updatetestIn order to be output, the output is,
Figure BDA0002010777970000125
Figure BDA0002010777970000126
constructing a new training sample by using the formula, and training the wavelet neural network; at network initialization, the new confidence level is utilized
Figure BDA0002010777970000127
And setting an initial weight value between the network input layer and the hidden layer. In this example, the association rules and models are updated every 10 data updates using the first 300 data of the test set.
And 5: and predicting abnormal working conditions based on the updated wavelet network model, and continuously updating the association rule and the wavelet neural network model before predicting the occurrence of the abnormal working conditions. The step is realized by the following steps:
recording a preset abnormal working condition (failure) occurrence threshold value as omegapIn this example, the threshold value for the occurrence of an abnormal condition (failure) is ωp0.015. For the newly acquired data, l is performed using the model updated in step 4pAnd predicting, namely if the obtained target parameter predicted value exceeds a set threshold relative to the initial normal drift amount, determining that an abnormal working condition occurs. Before the prediction process is finished, returning to step 4 to update the association rule and the model every time U data are updated, wherein the value of U can be adjusted, and U is 10 in this example.
Table 1 association rules
Rule antecedents Rule clause Confidence level
Variable 13 Variable 7 0.7527
Variable 16 Variable 7 0.7446
Variable 36 Variable 7 0.7017
Variable 35 Variable 11 0.7513
Variable 36 Variable 11 0.7390
TABLE 2 Total prediction error Rate
Introducing dynamic association rules Introducing association rules Without introducing association rules
Variable 7 0.7602 1.0247 1.7423
Variable 11 0.8075 0.8309 1.1884
In this example, in order to verify the validity of the method used in this chapter, the prediction result of the dynamic association rule introduced is compared with the case where the association rule is not introduced and the static association rule is introduced, and the comparison between the prediction result and the prediction error is shown in fig. 3 to 6. In fig. 3 and 5, a vertical solid line is an actual failure time under a preset failure threshold, a vertical dotted line is a failure time prediction value of introducing a dynamic association rule, a vertical dotted line is a failure time prediction value of introducing a static association rule, and a vertical dotted line is a failure time prediction value of not introducing an association rule. In addition, to better quantify the prediction error, the present example calculates the total error rate of the two parameter predictions under different conditions, as shown in table 2.
From the prediction results (fig. 3 and 5), compared with the other two cases, the introduction of the dynamic association rule can obtain a more accurate abnormal operating condition prediction result, and especially compared with the case without the introduction of the association rule, the introduction of the dynamic association rule has obvious advantages from the prediction curve. It should be noted that, in fig. 5, the predicted value and the true value have a large deviation when the static association rule is introduced and the association rule is not introduced, which is actually because the failure actual value of the parameter is exactly at a certain extreme point, so that if a slight prediction error exists here, the abnormal operating condition prediction result has a large deviation. In practice, a number of different threshold overruns may be set to monitor the parameters to avoid the risk of such a situation. In addition, from the perspective of prediction error (fig. 4 and 6), compared with the case of introducing a static association rule, introducing a dynamic association rule can reduce the prediction error to a certain extent, and as can be seen from table 2, for the variable 7, the promotion effect of introducing the dynamic association rule is obvious, and for the variable 11, because the change of the association rule is not large, the effect is not obvious; it can be seen from fig. 4 and 6 that the prediction error can be significantly reduced by introducing the dynamic association rule compared to the case of not introducing the association rule, and it can also be more intuitively seen from table 2 that the introduction of the dynamic association rule has a significant advantage compared to the case of not introducing the association rule.

Claims (6)

1. An industrial process abnormal working condition prediction method based on dynamic association rule mining is characterized by comprising the following specific steps:
step 1: carrying out data preprocessing on a measured value time sequence acquired by a sensor in an industrial process based on a sliding window, and carrying out piecewise linearization representation, clustering and symbolization on the time sequence to generate a transaction set suitable for association rule mining;
step 2: generating a frequent item set by adopting a two-stage frequent item set generation strategy, and mining association rules of every two parameters;
and step 3: performing association rule mining by using an initial data set, and training an initial wavelet neural network model based on an initial association rule mining result;
and 4, step 4: sliding a data window based on data updating, and updating an association rule and a wavelet neural network model for a new data window;
and 5: predicting abnormal working conditions based on the updated wavelet network model, and continuously updating the association rule and the wavelet neural network model before predicting the occurrence of the abnormal working conditions;
the step 1 is specifically realized by the following substeps:
step 1.1: the sensor measurement sequence is recorded as
Figure FDA0002458389690000011
N is the number of sensors, K is the sequence length, K is 1,2,., K, the sliding window length is L, the sliding distance is l each time, and the sliding window is denoted as WkThe data contained in the window is
Figure FDA0002458389690000012
Step 1.2: and performing time series piecewise linearization representation, clustering and symbolization processing on each data window, wherein the specific process is as follows:
for each data window WkPerforming the following processes of 1.2.1-1.2.5:
1.2.1. setting the initial fitting starting point to
Figure FDA0002458389690000013
End point is
Figure FDA0002458389690000014
Fitting error threshold value is omegaE(ii) a Initializing a segmentation point count value 1;
1.2.2. for each fitting starting point in turn
Figure FDA0002458389690000015
Performing step 1.1) -step 1.4):
1.1) first calculating end ═ start + h;
1.2) for data
Figure FDA0002458389690000016
Fitting by using a least square method, and calculating a fitting error ERR;
1.3) if the fitting error ERR is not greater than the fitting error threshold value omegaEIf h is h +1, skipping to step 1) again;
1.4) if the fitting error ERR is larger than the fitting error threshold value omegaEThen save the segmentation point
Figure FDA0002458389690000021
Figure FDA0002458389690000022
To Pi(ii) a Saving fitted line segments
Figure FDA0002458389690000023
Representing the fitted line segment by means of a triplet
Figure FDA0002458389690000024
And store to
Figure FDA0002458389690000025
kiWhich represents the slope of the line segment,
Figure FDA0002458389690000026
represents the span of the line segment on the time axis, riRepresenting a growth rate of the line segment data; resetting the fitting starting point start to be start + h and resetting h to be 2; updating count + 1;
1.2.3. circularly executing 1.2.2 until end is larger than k + L-1 to obtain a fitted linear time sequence
Figure FDA0002458389690000027
And segmentation point
Figure FDA0002458389690000028
Composed sequence of segmentation points Pi；
1.2.4. With Wk-1The clustering center of the window is the initial clustering center, and the triple sequence is subjected to K-means clustering algorithm
Figure FDA0002458389690000029
Clustering is carried out, and different symbols are distributed to the line segments of different classes to obtain symbolic sequences
Figure FDA00024583896900000210
Wherein, two line segments s are described by indexes based on Euclidean distance in the clustering processiAnd sjSimilarity between them:
Figure FDA00024583896900000211
wherein d isijI.e. representing a line segment siAnd sjSimilarity of (d)ijThe smaller the size, the more similar the change form of the two line segments, ωkAnd ωrRepresenting a weight;
1.2.5. for two operating parameters ViAnd VjRespectively obtaining the symbolized sequences
Figure FDA00024583896900000212
And
Figure FDA00024583896900000213
segment point sequence P combining two operating parametersiAnd PjAnd symbolizing the sequence of the combined segmentation points
Figure FDA00024583896900000214
And
Figure FDA00024583896900000215
performing segmentation reconstruction to obtain reconstructed symbolic sequence
Figure FDA00024583896900000216
And
Figure FDA00024583896900000217
from which a transaction set is formed, nij-1 is PiAnd PjThe number of the combined segmentation points.
2. The method for predicting the abnormal working condition of the industrial process based on the dynamic association rule mining as claimed in claim 1, wherein: the step 2 is specifically realized by the following substeps:
step 2.1: each transaction of the transaction set obtained by step 1 is marked as
Figure FDA00024583896900000218
In addition will
Figure FDA00024583896900000219
And
Figure FDA00024583896900000220
the line segment type symbols included in (1) are respectively marked as
Figure FDA00024583896900000221
And
Figure FDA00024583896900000222
miand mjAre respectively as
Figure FDA0002458389690000031
And
Figure FDA0002458389690000032
the number of line segment categories contained in (1); setting the minimum support threshold of the frequent item set generation process as min1；
Step 2.2: for an initial window of data W1And (3) performing association rule mining, wherein the specific process is as follows:
2.2.1) scanning the sensor measurement sequence dataset of all sensors to calculate the support of each term, let σ (-) denote the support count of a term or set of terms, initially 0; suppose that
Figure FDA0002458389690000033
Is denoted by the class symbol tkE represents i or j, t represents c or d;
for each one
Figure FDA0002458389690000034
Calculating σ (t)k)＝σ(tk)+1；
For each tkJudgment of
Figure FDA0002458389690000035
If yes, then confirmIs tkFor frequent 1-item sets, reserve tkAnd records the corresponding support count σ (t)k)；
2.2.2) use the resulting frequent 1-item set tkForm 2-item set and find frequent 2-item set, respectivelypAnd dqRespectively, the symbols of the original line segment class after the step 2.2.1)
Figure FDA0002458389690000036
And
Figure FDA0002458389690000037
the item retained in (1);
for each { cp,dqExecuting the operation: for each one exists in
Figure FDA0002458389690000038
C in (2)p,dq}, calculate σ ({ c)p,dq})＝σ({cp,dq}) + 1; if it is not
Figure FDA0002458389690000039
Not less than min1Then consider { cp,dqKeep { c) for frequent 2-item setp,dqAnd recording corresponding support degree counts;
2.2.3) use the frequent 2-item set { c) obtained in step 2.2.2p,dqEvery two operation parameters V are calculatediAnd VjThe support in the whole data set is calculated as follows: for every two operating parameters ViAnd VjSet of formed items { Vi,Vj}, calculate σ ({ V)i,Vj})＝sum(σ({cp,dq})) two parameters, associated rule support
Figure FDA00024583896900000310
And calculates σ (V)i)＝sum(σ(cp))；σ(Vj)＝sum(σ(dq))；
2.2.4) For each group { Vi,VjResults in the following association rules: vj→ViAnd Vi→Vj(ii) a For each association rule, calculate its confidence as
Figure FDA00024583896900000311
Step 2.3: for each new data window, the following process is adopted for frequent item set generation and association rule generation:
for each k>1 data window WkPerforming steps 2.3.1) to 2.3.3):
2.3.1) transaction to each sliding-out Window
Figure FDA0002458389690000041
Is marked as { ca1,da2}, judging { ca1,da2Whether the item is a frequent item set or not, if so, updating the sigma ({ c)a1,da2})＝σ({ca1,da2})-1；
2.3.2) transactions for each sliding-in window
Figure FDA0002458389690000042
Is marked as { cb1,db2}, judging { cb1,db2Whether the item set is frequent or not, if yes, update sigma ({ c)b1,db2})＝σ({cb1,db2}) +1, if not, in the data window WkInternally calculating the { cb1,db2Support count of σ ({ c)c1,dc2})；
2.3.3) for each { c }a1,da2Get it before
Figure FDA0002458389690000043
Update { c }a1,da2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cb1,db2Get it before
Figure FDA0002458389690000044
Update { c }b1,db2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set; for each { cc1,dc2Get it before
Figure FDA0002458389690000045
Update { c }c1,dc2Counting the support degree of the corresponding item set, and if the support degree is not met, rejecting the corresponding item set;
2.3.4) update the support and confidence of each association rule using steps 2.2.3) and 2.2.4).
3. The method for predicting the abnormal working condition of the industrial process based on the dynamic association rule mining as claimed in claim 2, wherein: the step 3 is specifically realized by the following substeps:
the preset prediction step length is recorded as lpThe association parameter extracted from the association rule mined from the training data set is { V }1,V2,...,VuU is the number of the extracted group of associated parameters; for each parameter ViThe measured value sequence is
Figure FDA0002458389690000046
Construct the following matrix ItrainFor initial neural network training inputs:
Figure FDA0002458389690000047
wherein, ItrainEach column in the training input sample is a training input sample; structural training output OtrainIn order to realize the purpose,
Figure FDA0002458389690000048
training a wavelet neural network by using a training input sample constructed by the formula (1); at network initialization, association rule V is utilizedi→VuCorresponding toInitial confidence ωiAn initial weight between the network input layer and the hidden layer is set, i ═ 1, 2.
4. The method for predicting the abnormal working condition of the industrial process based on the dynamic association rule mining as claimed in claim 3, wherein: the step 4 is specifically realized by the following substeps:
step 4.1: the updating method of the association rule comprises the following steps: recording c new data collected by the sensor in the running process of the equipment as
Figure FDA0002458389690000051
Using the latest L data in the newly acquired data
Figure FDA0002458389690000052
Update the association rule through step 2.3, thereby updating association rule Vi→VuThe corresponding confidence is recorded as
Figure FDA0002458389690000053
Step 4.2: the model updating method comprises the following steps: combining the training data and the newly acquired data to construct a new model training sample, i.e. a data set of
Figure FDA0002458389690000054
Construction matrix ItestConstructing O for input of neural network updatetestIn order to be output, the output is,
Figure FDA0002458389690000055
Figure FDA0002458389690000056
constructing a new training sample by using the formula, and training the wavelet neural network; at network initialization, the new confidence level is utilized
Figure FDA0002458389690000057
And setting an initial weight value between the network input layer and the hidden layer.
5. The method for predicting the abnormal working condition of the industrial process based on the dynamic association rule mining as claimed in claim 4, wherein the method comprises the following steps: the step 5 is specifically realized by the following substeps:
recording a preset abnormal working condition occurrence threshold value as omegapFor the latest collected data, l is performed by using the model updated in step 4pAnd predicting, namely if the obtained target parameter predicted value exceeds a set threshold relative to the initial normal drift amount, determining that an abnormal working condition occurs.
6. The method for predicting the abnormal working condition of the industrial process based on the dynamic association rule mining as claimed in claim 5, wherein: before the prediction process is finished, the association rules and the model are updated in step 4 every time a predetermined amount of data is updated.
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