CN111352408A - Multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor - Google Patents

Multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor Download PDF

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CN111352408A
CN111352408A CN202010164092.2A CN202010164092A CN111352408A CN 111352408 A CN111352408 A CN 111352408A CN 202010164092 A CN202010164092 A CN 202010164092A CN 111352408 A CN111352408 A CN 111352408A
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贺凯迅
苏照阳
钟麦英
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Shandong University of Science and Technology
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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
    • G05B23/00—Testing or monitoring of control systems or parts thereof
    • G05B23/02—Electric testing or monitoring
    • G05B23/0205—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults
    • G05B23/0259—Electric testing or monitoring by means of a monitoring system capable of detecting and responding to faults characterized by the response to fault detection
    • G05B23/0262—Confirmation of fault detection, e.g. extra checks to confirm that a failure has indeed occurred
    • 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
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Abstract

The invention discloses a multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor, which belongs to the field of industrial process monitoring. Then calculating the correlation degree of each sample in the training set and each working condition, and establishing an evidence information base; for a sample to be detected, K samples are selected from the training set by the K neighbor model, and evidence information corresponding to the K samples is synthesized by a D-S evidence theory to obtain a final detection result. The fault diagnosis model established based on evidence K nearest neighbor can be well adapted to the multi-working-condition running state of the system; the method can reduce the false alarm rate in the monitoring process and improve the accuracy of fault alarm.

Description

Multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor
Technical Field
The invention belongs to the field of industrial process monitoring, and particularly relates to a multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor.
Background
With the widespread use of DCS, it is becoming easier to collect data from industrial processes. The development of data mining and database technology also provides powerful technical support for the development and application of data-driven modeling methods in industrial processes. Data-driven process monitoring, control and system optimization are therefore widely used.
The boiler is one of the most important parts of a thermal power plant as a complex large energy conversion device, and the stable operation of the boiler is closely related to the quality of electric energy. However, the combustion process of the boiler is complex and changeable, and a precise model of the boiler is difficult to establish by utilizing the traditional mechanism analysis; meanwhile, the load of the boiler changes along with the external requirements, the operation mode of the boiler changes under different loads, and the operation process shows obvious multi-working-condition characteristics.
For conventional fault diagnosis methods, such as Principal Component Analysis (PCA), Partial Least Squares (PLS), etc., have been used for a long time for fault detection and diagnosis of industrial processes. Such methods are also considered to be very effective. However, it should be noted that the methods of PCA, PLS, etc. are formulated under the assumption that the process variables are independent, uniformly distributed and linearly related, and that the system has only a single normal operating condition. In actual industrial production, the assumed conditions are difficult to meet, the monitoring result is inaccurate, and a large number of false alarms occur.
Disclosure of Invention
Aiming at the technical problems, the invention provides a multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor, overcomes the defects of the prior art and has good effect.
In order to achieve the purpose, the invention adopts the following technical scheme:
a multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor comprises the following steps:
step 1: acquiring normal historical working condition data of an industrial process, taking the data as a training set, standardizing the training set, and calculating a standardized value;
step 2: dividing the standardized training set into a plurality of normal working conditions through a clustering algorithm;
and step 3: determining evidence information according to a relevancy criterion, and obtaining the relevancy of each training sample and each normal working condition as each training sample carries the evidence information, thereby establishing an evidence information base; the evidence information base is a set of evidence information carried by all normal samples;
and 4, step 4: calculating discount factor gamma of training samples according to local density of each training sampleiAdapting the fault boundary to the local characteristics of the training sample;
and 5: standardizing the data to be detected, and finding out k samples closest to the data to be detected in a training set;
step 6: and (4) synthesizing the evidence information of the k samples in the step (5) according to a D-S evidence theory Dempster' S integration rule to obtain a fault detection result.
Preferably, in step 1, the normalized value, normal operating condition data set, is calculated according to equation (1)
Figure BDA0002406780310000021
n is the number of normal working condition samples, i represents the ith sample, and p is the dimension of each sample:
Figure BDA0002406780310000022
wherein the content of the first and second substances,
Figure BDA0002406780310000023
is the actual value of the,
Figure BDA0002406780310000024
is a variable xjThe maximum value of (a) is,
Figure BDA0002406780310000025
is a variable xjThe minimum value of (a) is determined,
Figure BDA0002406780310000026
is a normalized value and j represents the sample dimension.
Preferably, the clustering algorithm in step 2 is any one of a K-means clustering method, a gaussian mixture model clustering method, and a density peak value clustering method.
Preferably, the relevancy criterion in step 3 is one or a combination of two methods selected from euclidean distance, mahalanobis distance and local density.
Preferably, in step 3, evidence information is calculated for each training sample according to equation (2):
Figure BDA0002406780310000027
wherein q represents the q-th normal operating condition, diqThe correlation degree of the ith sample and the qth normal working condition is shown, and c represents the total number of the normal working conditions;
Figure BDA0002406780310000028
represents the sum of the correlation of the ith sample with all the working conditions, mi({ωqAnd) represents the probability that the ith sample belongs to the qth normal condition.
Preferably, in step 4, the discount factor γ of the training samples is calculated according to equation (3)i：
Figure BDA0002406780310000029
Wherein D isiIs a sample xiAnd distance sample xiThe distances between the nearest k training samples are combined into a set, ε is a parameter within the quantile function.
Preferably, in step 6, the following steps are included:
step 6.1: calculating an adaptive discount factor gamma according to equation (4)s：
Figure BDA0002406780310000031
Wherein λ is used to adjust γsSize of (a), γiA discount factor for a training sample;
step 6.2: according to equation (5), the weight values of the training samples are calculated:
Figure BDA0002406780310000032
wherein d isiRepresenting Euclidean distance, gamma, of the sample to be measured and the training samplesIs an adaptive discount factor;
step 6.3: and (3) calculating a mass function of the sample to be tested belonging to each working condition according to the formula (6):
Figure BDA0002406780310000033
where α is the confidence of the training set, mi(A) The evidence information of the sample to be detected is obtained;
Figure BDA0002406780310000034
index set representing K training samples closest to the sample to be measured, dsiRepresenting the distance between the sample to be tested and the training sample;
step 6.4: synthesizing a mass function according to the formulas (7), (8) and (9) to obtain a diagnosis result;
Figure BDA0002406780310000035
Figure BDA0002406780310000036
Figure BDA0002406780310000037
wherein, K1As a normalization factor, ms(Ω) represents the failure probability of the test sample, ms({ωq} represents the probability that the test sample is subject to each condition.
The invention has the following beneficial technical effects:
(1) the fault diagnosis model established based on the K neighbor can be well adapted to the multi-working-condition running state of the system; (2) the method can reduce the false alarm rate in the monitoring process and improve the accuracy of fault alarm.
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FIG. 1 is a flow chart of the method of the present invention.
FIG. 2 is a schematic diagram of a projection of a training set clustering result on a two-dimensional space according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a fault detection result of the multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor according to the embodiment of the present invention.
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
the example data was generated from one coal mill in a power plant boiler system, for a total of 2880 samples and 28 variables. The fault interval is 2133 and 2500. The first 1000 normal samples are taken as a training set, and the rest samples are taken as a testing set. As shown in fig. 1, the method of the present invention includes the following two major stages: the method comprises the steps of respectively performing off-line modeling and on-line monitoring, wherein the off-line modeling stage specifically comprises the following steps:
step 1: standardizing the training set;
Figure BDA0002406780310000041
in the above formula, the first and second carbon atoms are,
Figure BDA0002406780310000042
is the actual value of the,
Figure BDA0002406780310000043
is a variable xjThe maximum value of (a) is,
Figure BDA0002406780310000044
is a variable xjThe minimum value of (a) is determined,
Figure BDA0002406780310000045
is a normalized value and j represents the sample dimension.
Step 2: projecting a high-dimensional data space composed of process data to a low-dimensional feature subspace by using PCA (principal component analysis), as shown in FIG. 2; observing the optimal clustering number, clustering the training set by a K-means clustering algorithm, wherein a parameter K is 5, and dividing the normal working condition into two working conditions of w1 and w 2;
and step 3: respectively calculating the central points of w1 and w2, calculating the correlation degree of each training set with w1 and w2 to establish an evidence information base, and calculating the discount factor gamma of each training samplei；
Figure BDA0002406780310000046
In the above formula, DiIs a training sample xiAnd (5) selecting a distance set of the training samples from the K nearest neighbor model, wherein the parameter epsilon is 0.85.
The on-line monitoring stage mainly comprises:
step 1: for a sample x to be measurediFirst, normalization is performed. Finding the 5 nearest neighbor samples in the training set constitutes the set Θ.
Step 2: calculating the sample x to be tested according to the discount factor corresponding to each training set in the thetaiAdaptive discount factor gamma ofs。
Figure BDA0002406780310000051
In the above formula, the parameter λ is 0.2, γiA discount factor for a training sample;
and step 3: calculating a sample x to be measurediAnd the weight between each training set within Θ:
Figure BDA0002406780310000052
in the above formula, diRepresenting Euclidean distance, gamma, of the sample to be measured and the training samplesIs an adaptive discount factor;
and 4, step 4: calculating a mass function of the sample to be detected belonging to each working condition:
Figure BDA0002406780310000053
wherein, the parameter α is the setting of the training setReliability, mi(A) Evidence information of training samples;
and 5: and synthesizing the evidence carried by each training set in the theta by using Dempster's integration rule to obtain a final detection result. When m is specifieds(Ω) ═ 0.2 as the early warning boundary of the fault, msThe fault boundary is set to 0.9 (Ω). The monitoring results are shown in fig. 3;
Figure BDA0002406780310000054
Figure BDA0002406780310000055
Figure BDA0002406780310000056
wherein, K1As a normalization factor, ms(Ω) represents the failure probability of the test sample, ms({ωq} represents the probability that the test sample is subject to each condition.
The above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and variations, modifications, additions and substitutions which may be made by those skilled in the art within the spirit of the present invention are within the scope of the present invention.

Claims (7)

1. A multi-working-condition process industrial process fault detection method based on evidence K nearest neighbor is characterized by comprising the following steps: the method comprises the following steps:
step 1: acquiring normal historical working condition data of an industrial process, taking the normal historical working condition data as a training set, standardizing the training set, and calculating a standardized value;
step 2: dividing the standardized training set into a plurality of normal working conditions through a clustering algorithm;
and step 3: determining evidence information according to a relevancy criterion, and obtaining the relevancy of each training sample and each normal working condition as each training sample carries the evidence information, thereby establishing an evidence information base; the evidence information base is a set of evidence information carried by all normal samples;
and 4, step 4: calculating discount factor gamma of training samples according to local density of each training sampleiAdapting the fault boundary to the local characteristics of the training sample;
and 5: standardizing the data to be detected, and finding out k samples closest to the data to be detected in a training set;
step 6: and (4) synthesizing the evidence information of the k samples in the step (5) according to a D-S evidence theory Dempster' S integration rule to obtain a fault detection result.
2. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: in step 1, the normalized value, normal condition data set, is calculated according to equation (1)
Figure FDA0002406780300000011
n is the number of normal working condition samples, i represents the ith sample, and p is the dimension of each sample:
Figure FDA0002406780300000012
wherein the content of the first and second substances,
Figure FDA0002406780300000013
is the actual value of the,
Figure FDA0002406780300000014
is a variable xjThe maximum value of (a) is,
Figure FDA0002406780300000015
is a variable xjThe minimum value of (a) is determined,
Figure FDA0002406780300000016
is a normalized value, j represents the sample dimension。
3. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: the clustering algorithm in the step 2 is any one of a K-means clustering method, a Gaussian mixture model clustering method and a density peak value clustering method.
4. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: and the relevancy criterion in the step 3 is one or a combination of two methods of Euclidean distance, Mahalanobis distance and local density.
5. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: in step 3, evidence information is calculated for each training sample according to equation (2):
Figure FDA0002406780300000021
wherein q represents the q-th normal operating condition, diqThe correlation degree of the ith sample and the qth normal working condition is shown, and c represents the total number of the normal working conditions;
Figure FDA0002406780300000022
represents the sum of the correlation of the ith sample with all the working conditions, mi({ωqAnd) represents the probability that the ith sample belongs to the qth normal condition.
6. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: in step 4, according to equation (3), the discount factor γ of the training samples is calculatedi：
Figure FDA0002406780300000023
Wherein D isiIs a sample xiAnd distance sample xiThe distances between the nearest k training samples are combined into a set, ε is a parameter within the quantile function.
7. The evidence K nearest neighbor based multi-condition process industrial process fault detection method according to claim 1, wherein: in step 6, the method comprises the following steps:
step 6.1: calculating an adaptive discount factor gamma according to equation (4)s：
Figure FDA0002406780300000024
Wherein λ is used to adjust γsSize of (a), γiA discount factor for a training sample;
step 6.2: according to equation (5), the weight values of the training samples are calculated:
Figure FDA0002406780300000025
wherein d isiRepresenting Euclidean distance, gamma, of the sample to be measured and the training samplesIs an adaptive discount factor;
step 6.3: and (3) calculating a mass function of the sample to be tested belonging to each working condition according to the formula (6):
Figure FDA0002406780300000026
where α is the confidence of the training set, mi(A) The evidence information of the sample to be detected is obtained;
Figure FDA0002406780300000027
index set representing K training samples closest to the sample to be measured, dsiRepresenting the distance between the sample to be tested and the training sample;
step 6.4: synthesizing a mass function according to the formulas (7), (8) and (9) to obtain a diagnosis result;
Figure FDA0002406780300000031
Figure FDA0002406780300000032
Figure FDA0002406780300000033
wherein, K1As a normalization factor, ms(Ω) represents the failure probability of the test sample, ms({ωq} represents the probability that the test sample is subject to each condition.
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