CN116933643A - Intelligent data monitoring method based on partial robust M regression and multiple interpolation - Google Patents
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Abstract
The invention discloses an intelligent data monitoring method based on partial robust M regression and multiple interpolation, which comprises an online monitoring method based on information multiple interpolation and variation information bottlenecks and an offline modeling method based on variation information bottlenecks and depth support vector data description, wherein the online monitoring method based on information multiple interpolation and variation information bottlenecks comprises data acquisition and preprocessing, data analysis and abnormal value missing data elimination filling, and the offline modeling method based on variation information bottlenecks and depth support vector data description comprises data acquisition and preprocessing, variation bottleneck processing and depth support vector processing. The invention overcomes the influence of abnormal values on the model by using a partial robust M regression method, and realizes effective extraction of output related features by parameterizing the model by using a deep learning and variation theory based on Bayesian reasoning.
Description
Technical Field
The invention relates to the field of process monitoring and fault detection, in particular to an intelligent data monitoring method based on partial robust M regression and multiple interpolation.
Background
Process monitoring (Statistical Process Monitoring, PM) is a quality control method that enables real-time monitoring and improvement of industrial processes by analyzing and interpreting data collected during the production process. Generally, PM consists of three phases: 1) Offline modeling, namely establishing a multivariate statistical analysis model by utilizing historical data under normal process conditions; 2) And monitoring the process on line, and detecting faults by using the model. The operation working condition and the working environment become more complex, and the failure probability is gradually increased
The operation working condition and the working environment become more complex, and the failure probability is gradually increased. In addition, the existence of the missing value in the normal training data can lead to inaccurate monitoring models, reduce the fault detection rate and increase the false alarm rate. Model-based process monitoring methods appear to be ineffective in the face of faults in modern industrial processes. Compared with the PM method based on the model, the PM method based on data driving does not depend on process mechanism knowledge, and has wide application prospect in actual PM production.
Therefore, the invention provides a process monitoring and fault detection method for filling missing data based on partial robust M regression and multiple interpolation, which aims to effectively solve the problem of data missing in an online monitoring stage.
Disclosure of Invention
The invention aims to provide an intelligent data monitoring method based on partial robust M regression and multiple interpolation so as to solve the problems in the background technology.
In order to achieve the above purpose, the present invention provides the following technical solutions: the intelligent data monitoring method based on the partial robust M regression and the multiple interpolation comprises an online monitoring method based on the information multiple interpolation and variation information bottleneck and an offline modeling method based on the variation information bottleneck and depth support vector data description, wherein the online monitoring method based on the information multiple interpolation and variation information bottleneck comprises the following steps:
data acquisition and preprocessing, namely arranging sensors at reasonable positions of equipment to be detected aiming at application scenes of process monitoring and fault detection under different data loss conditions so as to collect data; dividing the length of a data sample according to the sampling frequency of the sensor and the collected data quantity; performing fault type labeling on each original data sample to construct a training data set;
data analysis, namely dividing the acquired data into process data X= [ X ] according to specific application scenes 1 ,x 2 ,…,x n ] T And quality data y= [ Y ] 1 ,y 2 ,…,y n ] T If Y is a single quality variable, abnormal values in X and Y can be removed directly by using partial robust M regression; for a plurality of quality variables, converting the quality variables into single quality variables by a weighting method, and then processing the single quality variables by using a partial robust M regression;
reject outliers: normalizing the process data X and the quality data Y; by calculating bad lever weightsHigh residual weight->And general weight w i Initializing a weight matrix W; calculating weighted process data X W =wx and weighted quality data Y W WY, where w=diag (W);
filling in missing data: based on bayesian reasoning, a number of possible interpolations are generated taking into account the uncertainty of the missing data, thus forming a number of complete data sets. Then, carrying out the same processing on each data set, obtaining an estimated value through comprehensive analysis, and completing statistical inference;
the offline modeling method based on the variation information bottleneck and the depth support vector data description comprises the following steps:
acquiring and preprocessing data, acquiring sensor historical data, and dividing preprocessed data X' into: x' = [ X 1 ',X 2 ',…,X b ',…,X B ']Where B is the number of process variables and B is the number of blocks, for each block, the corresponding process data X is used b 'and quality data Y' to train a variation information bottleneck model;
variable bottleneck processing, useExtracting quality related characteristics from the variation information bottleneck: on the basis of an information bottleneck criterion, parameterizing the model by utilizing a deep learning and variation theory; in order to realize effective output related feature extraction under the condition of data missing, an encoder q is designed in a variation information bottleneck model φ (v|x) and decoder p θ (y|x), wherein phi and theta are the variation parameter and the generation parameter, respectively; obtaining quality related characteristics v by solving a mean square error loss function b ；
Depth support vector processing, constructing depth support vector data description for block level monitoring: by using nuclear skills v b Training depth support vector data description model And minimizing the supersphere volume; phi (g) is a deep neural network with L hidden layers and weights M. The depth support vector data description model penalizes the distance from phi (g) to the hyper-sphere center c for the first term, and the last term is a network weight attenuation regular term with the hyper-parameter lambda; the neural network of the depth support vector data description model adopts a two-layer hidden layer design, and the dimension and super parameters of proper hidden features are set, and the confidence level is set to alpha=0.99; optimizing weight matrix M by random gradient descent algorithm b So that v b Hyper-sphere center c capable of being clustered in feature space b Around.
Preferably, the step of filling missing data in the online monitoring method specifically includes:
a: defining observed data Z in industrial process after eliminating abnormal value obs And missing data Z obs Order-making Select reasonable initial value to replace Z mis ；
b: constructing posterior distribution P (theta|Z) of variation information bottleneck model parameter theta obs )＝∫P(θ|Z obs ,Z mis )P(Z obs ,Z mis )dZ mis And given Z obs Z of (2) mis The conditional density P (Z) mis |Z obs )＝∫P(Z mis |Z obs ,θ)P(θ|Z obs )dθ；
c: from the conditional density P of θ given Z (θ|z obs ,Z mis ) The initial parameter θ, the posterior distribution P (θ|z obs )；
d: based on the current parameter theta i Based on the second step, missing data is calculated(i+1) th Iterating for the second time;
e: from the density of conditions(i+1) th Calculating estimation parameter of variation information bottleneck model in iteration>
f: repeating the step c and the step d to generate a Markov chainThe distribution of the missing data is given by a Monte Carlo method; thereby obtaining a complete data set Z ' = [ X ', Y ] ']Wherein X 'and Y' are complete process data and outlier-free quality data, respectively.
Compared with the prior art, the invention has the beneficial effects that:
the invention overcomes the influence of abnormal values on the model by using a partial robust M regression method, and constructs a plurality of complete data sets by taking uncertainty of missing data into consideration based on Bayesian reasoning; based on the information bottleneck criterion, the model is parameterized by utilizing the deep learning and variation theory, so that effective extraction of output related features is realized.
In summary, the method of the invention can be realized:
1) The invention can effectively solve the problem of data missing in the online monitoring stage of the nonlinear process;
2) The invention can reduce false alarm rate, improve fault detection rate and detect faults in early stage with shorter detection time delay;
3) The method can also be used for solving various types of missing data scenes such as classification, process control and the like, and has wide application prospect.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
fig. 2 is a schematic diagram of a network representation of deep support vector data description in embodiment 2 of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1: referring to fig. 1, the present invention provides a technical solution: an on-line monitoring method based on information multiple interpolation and variation information bottleneck comprises the following steps:
and a data acquisition step: aiming at application scenes of process monitoring and fault detection under different data loss conditions, arranging sensors at reasonable positions in an industrial process to collect data; performing fault type labeling on each original data sample to construct a training data set;
data primary analysis: dividing the acquired data into process data X= [ X ] 1 ,x 2 ,…,x n ] T And quality data y= [ Y ] 1 ,y 2 ,…,y n ] T . If Y is a single quality variable, the outliers in X and Y can be directly eliminated by using partial robust M regressionThe method comprises the steps of carrying out a first treatment on the surface of the For a plurality of quality variables, the quality variables can be converted into single quality variables by a weighting method, and then the single quality variables are processed by using partial robust M regression;
and (3) eliminating abnormal values: normalizing the process data X and the quality data Y; poor lever weight by giving different weights to samples using a partial robust M regression methodHigh residual weight->And general weight w i The weight matrix W is initialized, and the influence of an abnormal value on the model is overcome; calculating weighted process data X W =wx and weighted quality data Y W WY, where w=diag (W).
The missing data filling steps are as follows: based on bayesian reasoning, a number of possible interpolations are generated taking into account the uncertainty of the missing data, thus forming a number of complete data sets. Then, the same processing is carried out on each data set, an estimated amount is obtained through comprehensive analysis, and statistical inference is completed.
The method comprises the following specific steps:
a: defining observed data Z in industrial process after eliminating abnormal value obs And missing data Z obs Order-makingSelect reasonable initial value to replace Z mis ；
b: constructing posterior distribution P (theta|Z) of variation information bottleneck model parameter theta obs )＝∫P(θ|Z obs ,Z mis )P(Z obs ,Z mis )dZ mis And given Z obs Z of (2) mis The conditional density P (Z) mis |Z obs )＝∫P(Z mis |Z obs ,θ)P(θ|Z obs )dθ；
c: from the conditional density P of θ given Z (θ|z obs ,Z mis ) The initial parameter theta is used for calculating posterior distribution in an iterative modeP(θ|Z obs )；
d: based on the current parameter theta i Based on the second step, missing data is calculated(i+1) th Iterating for the second time;
e: from the density of conditions(i+1) th Calculating estimation parameter of variation information bottleneck model in iteration>
f: repeating the step c and the step d to generate a Markov chainThe distribution of missing data is given by the monte carlo method. Thereby obtaining a complete data set Z ' = [ X ', Y ] ']Wherein X 'and Y' are complete process data and outlier-free quality data, respectively.
Example 2: referring to fig. 1-2, the present invention provides a technical solution: an offline modeling method based on variation information bottleneck and depth support vector data description comprises the following steps:
data preprocessing: based on prior knowledge and expert experience, the preprocessed data X' is divided into: x' = [ X 1 ',X 2 ',…,X b ',…,X B ']B is the number of process variables and B is the number of blocks. For each block, corresponding process data X is used b 'and quality data Y' to train a variation information bottleneck model;
extracting quality related features by using variation information bottlenecks: on the basis of an information bottleneck criterion, parameterizing the model by utilizing a deep learning and variation theory; in order to realize effective output related feature extraction under the condition of data missing, an encoder q is designed in a variation information bottleneck model φ (v|x) and decoder p θ (y|x) wherein phi and theta are variation parameters, respectivelyAnd generating parameters; obtaining quality related characteristics v by solving a mean square error loss function b ；
Constructing a depth support vector data description for block level monitoring: by using nuclear skills v b Training depth support vector data description modelAnd minimizing the supersphere volume; phi (g) is a deep neural network with L hidden layers and weights M. The depth support vector data description model penalizes the distance from phi (g) to the hyper-sphere center c for the first term, and the last term is a network weight attenuation regular term with the hyper-parameter lambda; the neural network of the depth support vector data description model adopts a two-layer hidden layer design, and the dimension and super parameters of proper hidden features are set, and the confidence level is set to alpha=0.99; optimizing weight matrix M by random gradient descent algorithm b So that v b Hyper-sphere center c capable of being clustered in feature space b Around.
In this embodiment, in order to avoid collapse of the hypersphere (optimal radius R * =0), the present invention fixes the hypersphere center c+.0 as the mean of the initial network representation and uses an unbounded activation function in the network.
In this embodiment, for a new sample x new A monitoring statistic of block b is designed to Wherein v is b,new Is the new sample x in block b new Quality related features of (a).
In the present embodiment, construct S b (x) Probability density function of (c): probability density function of Wherein K (g) is a kernel function and H is a bandwidth; by solving integral formulasCalculate S b (x) Threshold of>Is S b (x) Is set to a threshold value of (2). If S b (x) Beyond the limit ofThe fault occurs in the corresponding zone b.
And constructing a global level monitoring model by fusing the block level monitoring results.
The monitoring performance of different faults in each mode was evaluated using the fault detection rate FDR, which was defined as:wherein S is a statistic and f represents a fault.
Although embodiments of the present invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made therein without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (2)
1. The intelligent data monitoring method based on the partial robust M regression and the multiple interpolation is characterized by comprising an online monitoring method based on the information multiple interpolation and the variation information bottleneck and an offline modeling method based on the variation information bottleneck and the depth support vector data description, wherein the online monitoring method based on the information multiple interpolation and the variation information bottleneck comprises the following steps:
data acquisition and preprocessing, namely arranging sensors at reasonable positions of equipment to be detected aiming at application scenes of process monitoring and fault detection under different data loss conditions so as to collect data; dividing the length of a data sample according to the sampling frequency of the sensor and the collected data quantity; performing fault type labeling on each original data sample to construct a training data set;
data analysis, namely dividing the acquired data into process data X= [ X ] according to specific application scenes 1 ，x 2 ，...，x n ] T And quality data y= [ Y ] 1 ，y 2 ，...，y n ] T If Y is a single quality variable, abnormal values in X and Y can be removed directly by using partial robust M regression; for a plurality of quality variables, converting the quality variables into single quality variables by a weighting method, and then processing the single quality variables by using a partial robust M regression;
reject outliers: normalizing the process data X and the quality data Y; by calculating bad lever weightsHigh residual weight->And general weight wi, initializing a weight matrix W; calculating weighted process data xw=wx and weighted quality data Y W WY, where w=diag (W);
filling in missing data: based on bayesian reasoning, a number of possible interpolations are generated taking into account the uncertainty of the missing data, thus forming a number of complete data sets. Then, carrying out the same processing on each data set, obtaining an estimated value through comprehensive analysis, and completing statistical inference;
the offline modeling method based on the variation information bottleneck and the depth support vector data description comprises the following steps:
acquiring and preprocessing data, acquiring sensor historical data, and dividing preprocessed data X' into: x '= [ X ]' 1 ，X′ 2 ，...，X′ b ，...，X′ B ]Where B is the number of process variables and B is the number of blocks, for each block, the corresponding process data X 'is used' b And quality data Y' to train a variational information bottleneck modelA shape;
and (3) processing the variation bottleneck, and extracting quality related characteristics by using the variation information bottleneck: on the basis of an information bottleneck criterion, parameterizing the model by utilizing a deep learning and variation theory; in order to realize effective output related feature extraction under the condition of data missing, an encoder q is designed in a variation information bottleneck model φ (v|x) and decoder p θ (y|x), wherein phi and theta are the variation parameter and the generation parameter, respectively; obtaining quality related characteristics v by solving a mean square error loss function b ；
Depth support vector processing, constructing depth support vector data description for block level monitoring: by using nuclear skills v b Training depth support vector data description model And minimizing the supersphere volume; phi (g) is a deep neural network with L hidden layers and weights M. The depth support vector data description model penalizes the distance from phi (g) to the hyper-sphere center c for the first term, and the last term is a network weight attenuation regular term with the hyper-parameter lambda; the neural network of the depth support vector data description model adopts a two-layer hidden layer design, and the dimension and super parameters of proper hidden features are set, and the confidence level is set to alpha=0.99; optimizing weight matrix M by random gradient descent algorithm b So that v b Hyper-sphere center c capable of being clustered in feature space b Around.
2. The intelligent data monitoring method based on partial robust M regression and multiple interpolation according to claim 2, wherein: the step of filling the missing data in the online monitoring method specifically comprises the following steps:
a: defining observed data Z in industrial process after eliminating abnormal value obs And missing data Z obs Order-making Select reasonable initial value to replace Z mis ；
b: constructing posterior distribution P (theta|Z) of variation information bottleneck model parameter theta obs )＝∫P(θ|Z obs ，Z mis )P(Z obs ，Z mis )dZ mis And given Z obs Z of (2) mis The conditional density P (Z) mis |Z obs )＝∫P(Z mis |Z obs ，θ)P(θ|Z obs )dθ；
c: from the conditional density P of θ given Z (θ|z obs ，Z mis ) The initial parameter θ, the posterior distribution P (θ|z obs )；
d: based on the current parameter theta i Based on the second step, missing data is calculated(i+1) th Iterating for the second time;
e: from the density of conditions(i+1) th Calculating estimation parameter of variation information bottleneck model in iteration>
f: repeating the step c and the step d to generate a Markov chainThe distribution of the missing data is given by a Monte Carlo method; thereby obtaining a complete data set Z ' = [ X ', Y ] ']Wherein X 'and Y' are complete process data and outlier-free quality data, respectively.
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| CN117609737B (en) * | 2024-01-18 | 2024-03-19 | 中国人民解放军火箭军工程大学 | Method, system, equipment and medium for predicting health state of inertial navigation system |
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