CN111079857B - Sewage treatment process fault monitoring method based on overcomplete width learning model - Google Patents

Sewage treatment process fault monitoring method based on overcomplete width learning model Download PDF

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CN111079857B
CN111079857B CN201911402093.XA CN201911402093A CN111079857B CN 111079857 B CN111079857 B CN 111079857B CN 201911402093 A CN201911402093 A CN 201911402093A CN 111079857 B CN111079857 B CN 111079857B
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常鹏
王凯
王普
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Abstract

The invention discloses a sewage treatment process fault monitoring method based on an overcomplete width learning model. The invention comprises two stages of off-line training and on-line monitoring. "offline training" includes: firstly, setting labels for collected normal data and partial fault data, carrying out normalization processing on training data, wherein the labels are in a normal state and the labels are in a fault state, extracting a unmixed matrix of the normal data by an overcomplete independent component analysis method, calculating an independent component matrix of the training data, and establishing an offline training model by using a width learning system. "on-line monitoring" includes: and normalizing and labeling the newly acquired data, wherein the label of 0 is in a normal state, the label of 1 is in a fault state, and online monitoring is performed by using a parameter model of an offline model. The invention does not need excessive network layers, has rapid fault monitoring and high monitoring accuracy.

Description

Sewage treatment process fault monitoring method based on overcomplete width learning model
Technical Field
The invention relates to the technical field of fault diagnosis based on data driving, in particular to a fault diagnosis technology aiming at a sewage treatment process. The invention relates to a specific application of a data driving-based method in the aspect of fault monitoring in a sewage treatment process.
Background
In recent decades, with the continuous development of economy and society, more and more sewage is produced in industrial production and social life, if the untreated sewage directly flows into water sources and lands of people, serious environmental pollution can be caused, if people drink the water carelessly, serious physical diseases can be even caused, and life safety is threatened. The water resources in China are abundant in total, but are unevenly distributed and have less people and average possession, and the research on sewage treatment and recycling technology is widely focused due to huge social benefits and environmental benefits. The sewage treatment process has the characteristics of strong nonlinearity, dynamic property, persistence and the like, and in order to ensure the high efficiency and stability of the sewage treatment process, it is necessary to establish an effective process monitoring scheme to timely detect the abnormal state in the sewage treatment process.
Currently, multivariate statistical techniques have been widely used for process monitoring of wastewater treatment processes, such as principal component analysis (Principal Component Analysis, PCA) and independent component analysis (Independent Component Analysis, ICA) and partial least squares analysis (Partial Least Squares, PLS), among others. Meanwhile, the process data does not always follow Gaussian distribution well, the ICA algorithm can extract non-Gaussian information in the data, but independent components extracted by the ICA algorithm are unstable. With the continued development of deep learning techniques, many people have applied deep learning techniques to process monitoring of wastewater treatment processes, such as deep belief networks (Deep Belief Networks, DBN), deep boltzmann machines (Deep Boltzmann Machines, DBM), convolutional neural networks (Convolutional neural Networks, CNN), and the like. Although most deep neural networks have strong fault monitoring capability for sewage treatment processes, the deep neural networks have complex structures and involve a large number of super-parameters, and the super-parameters of the deep neural networks are complex to set and adjust. On the other hand, in order to obtain higher accuracy in the application, the depth model often needs to be increased in network layer number or parameters, which makes the training process of the depth model often consume a lot of time and a lot of computing resources.
Disclosure of Invention
In order to achieve rapid and efficient process monitoring of a wastewater treatment process, the invention provides a wastewater treatment process fault monitoring method of an overcomplete width learning model integrating an overcomplete independent component analysis (Overcomplete Independent Component Analysis, OICA) and a width learning model (Broad Learning Model, BLM). The OICA and BLM algorithms were proposed by Anastasia Podosinnikova et al at the university of ma and Chen Junlong professor et al at the university of australia, respectively. Firstly, in order to extract non-Gaussian information in data, an OICA algorithm is used for obtaining a unmixed matrix of normal data in an offline training stage, the obtained unmixed matrix is used for calculating a stable independent component matrix containing the non-Gaussian information of training data, the independent component matrix of the training data and a label corresponding to the independent component matrix are used for training a width learning model, and finally, the online data is monitored by using a parameter model obtained through offline training to judge the running state of the current online data.
The invention adopts the following technical scheme and implementation steps:
A. offline training stage:
1) Collecting normal data and fault data in sewage treatment process to form training data set X train . Wherein, the normal data is marked with a '0' label, the fault data is marked with a '1' label, and the label matrix is marked with Y train 。X train Each group of data comprises K sampling moments and M variables. For each sampling instant
Figure BDA0002344728210000021
x i,j The value of the j-th variable representing the i-th sampling instant. The training data is marked as follows:
Figure BDA0002344728210000022
normal and normal-label represent normal data and labels thereof, and fault-label represent fault data and labels thereof, respectively;
2) The training data are standardized:
first, the mean and standard deviation of normal data are calculated, wherein the mean value at all times of the j-th variable is
Figure BDA0002344728210000023
The j-th variableStandard deviation of all moments of (2) is +.>
Figure BDA0002344728210000024
Figure BDA0002344728210000025
Finally, normalized training data are obtained +.>
Figure BDA0002344728210000026
3) Calculating standardized training data by using OICA algorithm
Figure BDA0002344728210000027
The unmixed matrix D of normal data, and the independent component matrix S of training data is obtained by using the D train Will S train Data tag Y corresponding to the data tag Y train Labeling:
Figure BDA0002344728210000028
wherein ,snormal And normal-label represent the independent component matrix of normal data and its label, s, respectively fault And fault-label respectively represent an independent component matrix of fault data and a label thereof;
4) Independent component matrix S obtained for OICA algorithm by using width learning model train And corresponding data tag Y train Training to obtain offline weight W m ：
Input matrix A of width learning model n Is formed by mapping node Z n And reinforcing node H j Two parts. Is provided with n mapping nodes and j enhancement nodes, which are formed by independent component matrix S train The mapping is carried out to obtain: z is Z i ＝φ(S train W ei +β ei ) Phi is a linear activation function, W ei Is a random weight, beta ei Is a random bias. Mapping to obtain a mapping node Z n ＝(Z 1 ,Z 2 ,…,Z n ). Similarly, enhance node H j ＝(H 1 ,H 2 ,…,H j ) Is formed by mapping node Z n ＝(Z 1 ,Z 2 ,…,Z n ) The mapping is carried out to obtain: h j ＝ζ(Z n W hj +β hj ) ζ is an activation function tanh, W hj Is a random weight, beta hj Is a random bias. A is that n ＝[Z n |H j ]Is an extended input matrix formed by combining all mapping nodes with enhancement nodes. 100 mapped nodes and 100 enhanced nodes are determined as the number of network nodes when training offline. Thus, the width learning model can be expressed as:
Y＝[φ(S train W e1 +β e1 ),…,φ(S train W en +β en )|ζ(Z n W h1 +β h1 ),…,ζ(Z n W hj +β hj )]W n
＝[Z 1 ,Z 2 ,…,Z n |H 1 ,…,H j ]W n
＝[Z n |H j ]W n
wherein ,
Figure BDA0002344728210000031
W n the connection weight of the width system is obtained by using a ridge regression method, lambda is a constraint parameter, I is a unit matrix, A n ＝[Z n |H j ]Y is the label output obtained by training;
5) According to the steps, training the width learning model by utilizing different fault type data to obtain over-complete width model subsystems of C different fault types;
B. on-line monitoring:
6) Collecting current sewage treatment process data X test As a test dataset.
7) Average and standard deviation vs. X of normal data obtained off-line test Standardized test data are obtained by standardized processing
Figure BDA0002344728210000032
8) Calculation of
Figure BDA0002344728210000033
Independent component matrix S of (2) test ：/>
Figure BDA0002344728210000034
Wherein D is a unmixed matrix obtained in an off-line stage;
9) Matrix S of independent components test Respectively input into the C overcomplete width model subsystems. In the C overcomplete width model subsystems, test data S is obtained by utilizing network parameters obtained through offline training test Mapping to characteristic nodes and enhancement nodes, and then utilizing a network weight matrix W obtained by offline training n For online data S test Reconstructing to obtain a reconstructed data tag
Figure BDA0002344728210000035
Figure BDA0002344728210000036
wherein ,
Figure BDA0002344728210000037
Figure BDA0002344728210000038
Figure BDA0002344728210000039
10 Reconstructed data tag obtained by calculating C overcomplete width model subsystems
Figure BDA00023447282100000310
If->
Figure BDA00023447282100000311
If the value of (1) is 1, the fault is considered to occur, and an alarm is given; otherwise, it is normal.
Advantageous effects
Compared with the prior art, the method has the advantages that the over-complete width learning model is utilized to carry out offline training on the acquired normal data and fault data to obtain the weight and bias matrix, then the parameters obtained by training are used to monitor the online data, more characteristic information can be extracted compared with the traditional multivariate statistical analysis method, a large number of layers are not required to be stacked like a deep network, complex super-parameters are not required, the complexity of the model is low, the monitoring response is rapid, and the monitoring accuracy is high.
Drawings
FIG. 1 is a block diagram of a breadth-learning model network;
FIG. 2 is a monitoring graph of single variable faults under sunny conditions;
FIG. 3 is a graph of monitoring single variable faults under drought conditions;
FIG. 4 is a graph of monitoring a composite variable fault under sunny conditions;
FIG. 5 is a graph of monitoring composite univariate faults under drought conditions;
Detailed Description
The sewage treatment process not only comprises various physical changes, but also comprises complex chemical changes and biochemical reactions, which lead to complex process flows of the sewage treatment process, and simultaneously, the sewage treatment process anomaly monitoring brings great challenges. The invention adopts a 'simulation benchmark model' (Benchmark Simulation Model, BSM 1) developed by International Water Association (IWA) to simulate the actual sewage treatment process in real time. The model consists of five reaction tanks (5999 m 3) and one secondary sedimentation tank (6000 m 3), and three aeration tanks. The aeration tank has 10 layers, the depth is 4 meters, the occupied area is 1500m < 2 >, and the reaction process has internal reflux and external reflux. The average sewage treatment flow rate is 20000m3/d, and the chemical oxygen demand is 300mg/l. The experiment uses single variable faults and compound variable faults for monitoring, the single variable faults are changes of the maximum growth rate uh of the heterotrophic bacteria, the compound variable faults are values of the maximum growth rate uh of the heterotrophic bacteria and toxic impact bh, the values of the maximum growth rate uh of the heterotrophic bacteria and the toxic impact bh are changed at the same time, the values of the maximum growth rate uh of the heterotrophic bacteria and the values of the toxic impact bh are 4 under the normal operating condition uh, and the values of the toxic impact bh are 0.3 under the normal operating condition bh. The simulation platform collects 1344 sampling point data, and each sampling point contains 16 variables. The variables used for monitoring are shown in table 1 and changed to 2 to a fault setting condition.
Table 1 monitoring the variables used
Figure BDA0002344728210000041
Figure BDA0002344728210000051
Table 2 fault set-up conditions
Figure BDA0002344728210000052
The specific statement that the method of the invention is applied to the offline training and online monitoring of the BSM1 simulation platform is as follows:
A. offline training stage:
1) Collecting normal data and fault data in sewage treatment process to form training data set X train . Wherein, the normal data is marked with a '0' label, the fault data is marked with a '1' label, and the label matrix is marked with Y train 。X train Each group of data comprises K sampling moments and M variables. For each sampling instant
Figure BDA0002344728210000053
x i,j The value of the j-th variable representing the i-th sampling instant. The training data is marked as follows:
Figure BDA0002344728210000054
normal and normal-label represent normal data and labels thereof, and fault-label represent fault data and labels thereof, respectively;
2) The training data are standardized:
first, the mean and standard deviation of normal data are calculated, wherein the mean value at all times of the j-th variable is
Figure BDA0002344728210000055
The standard deviation of all moments of the jth variable is +.>
Figure BDA0002344728210000056
Figure BDA00023447282100000510
Finally, normalized training data are obtained +.>
Figure BDA0002344728210000057
3) Calculating standardized training data by using OICA algorithm
Figure BDA0002344728210000058
The unmixed matrix D of normal data, and the independent component matrix S of training data is obtained by using the D train Will S train Data tag Y corresponding to the data tag Y train Labeling:
Figure BDA0002344728210000059
wherein ,snormal And normal-label represent the independent component matrix of normal data and its label, s, respectively fault And fault-label respectively represent an independent component matrix of fault data and a label thereof;
4) Independent component matrix S obtained for OICA algorithm by using width learning model train And corresponding data tag Y train Training to obtain offline weight W m ：
Input matrix A of width learning model n Is formed by mapping node Z n And reinforcing node H j Two parts. Is provided withHaving n mapping nodes and j enhancement nodes, which are formed by independent component matrix S train The mapping is carried out to obtain: z is Z i ＝φ(S train W ei +β ei ) Phi is a linear activation function, W ei Is a random weight, beta ei Is a random bias. Mapping to obtain a mapping node Z n ＝(Z 1 ,Z 2 ,…,Z n ). Similarly, enhance node H j ＝(H 1 ,H 2 ,…,H j ) Is formed by mapping node Z n ＝(Z 1 ,Z 2 ,…,Z n ) The mapping is carried out to obtain: h j ＝ζ(Z n W hj +β hj ) ζ is an activation function tanh, W hj Is a random weight, beta hj Is a random bias. A is that n ＝[Z n |H j ]Is an extended input matrix formed by combining all mapping nodes with enhancement nodes. 100 mapped nodes and 100 enhanced nodes are determined as the number of network nodes when training offline. Thus, the width learning model can be expressed as:
Y＝[φ(S train W e1 +β e1 ),…,φ(S train W en +β en )|ζ(Z n W h1 +β h1 ),…,ζ(Z n W hj +β hj )]W n
＝[Z 1 ,Z 2 ,…,Z n |H 1 ,…,H j ]W n
＝[Z n |H j ]W n
wherein ,
Figure BDA0002344728210000061
W n the connection weight of the width system is obtained by using a ridge regression method, lambda is a constraint parameter, I is a unit matrix, A n ＝[Z n |H j ]Y is the label output obtained by training;
5) According to the steps, training the width learning model by utilizing different fault type data to obtain over-complete width model subsystems of C different fault types;
B. on-line monitoring:
6) Collecting current sewage treatment process data X test As a test dataset.
7) Average and standard deviation vs. X of normal data obtained off-line test Standardized test data are obtained by standardized processing
Figure BDA0002344728210000062
8) Calculation of
Figure BDA0002344728210000063
Independent component matrix S of (2) test ：/>
Figure BDA0002344728210000064
Wherein D is a unmixed matrix obtained in an off-line stage;
9) Matrix S of independent components test Respectively input into the C overcomplete width model subsystems. In the C overcomplete width model subsystems, test data S is obtained by utilizing network parameters obtained through offline training test Mapping to characteristic nodes and enhancement nodes, and then utilizing a network weight matrix W obtained by offline training n For online data S test Reconstructing to obtain a reconstructed data tag
Figure BDA0002344728210000071
Figure BDA0002344728210000072
wherein ,
Figure BDA0002344728210000073
/>
Figure BDA0002344728210000074
Figure BDA0002344728210000075
10 Reconstructed data tag obtained by calculating C overcomplete width model subsystems
Figure BDA0002344728210000076
If->
Figure BDA0002344728210000077
If the value of (1) is 1, the fault is considered to occur, and an alarm is given; otherwise, it is normal.
The steps are specific application of the method in the field of fault monitoring of the BSM1 simulation platform. To verify the effectiveness of the method, each training was performed using one batch of normal data and 3 batches of fault data, and experiments at the online monitoring stage were performed using 4 batches of test data, respectively. The experimental results obtained are shown in fig. 1 to 4. If the value of the ordinate in the graph is 1, the sewage treatment process is considered to be faulty at the moment; if the ordinate value is 0, the sewage treatment process is normally operated. From the monitoring graph, it can be seen that the overcomplete width learning model responds more rapidly to the monitoring of four faults. Meanwhile, as can be seen from table 3, fault monitoring of the overcomplete width learning model is free from false alarm and less in missing alarm.
TABLE 3 monitoring Effect
Figure BDA0002344728210000078
/>

Claims (3)

1. A sewage treatment process fault monitoring method based on an overcomplete width learning model is characterized by comprising two stages of off-line training and on-line monitoring, and comprises the following specific steps:
A. offline training stage:
1) Collecting normal data and fault data in sewage treatment process to form training data set X train Wherein, the normal data is marked with a '0' label, the fault data is marked with a '1' label, and the label matrix is marked with Y train ；X train Comprises N groups of data, each group of data comprises K sampling moments and M variables, and each sampling moment
Figure QLYQS_1
x i，j The value of the jth variable representing the ith sample time, the training data is marked as follows:
Figure QLYQS_2
normal and normal-label represent normal data and labels thereof, and fault-label represent fault data and labels thereof, respectively;
2) The training data are standardized:
3) Calculating standardized training data by using OICA algorithm
Figure QLYQS_3
The unmixed matrix D of normal data, and the independent component matrix S of training data is obtained by using the D train Will S train Data tag Y corresponding to the data tag Y train Labeling:
Figure QLYQS_4
wherein ,snomal And normal-label represent the independent component matrix of normal data and its label, s, respectively fault And fault-label respectively represent an independent component matrix of fault data and a label thereof;
4) Independent component matrix S obtained for OICA algorithm by using width learning model train And corresponding data tag Y train Training to obtain offline weight W m ：
Input matrix A of width learning model n Is formed by mapping node Z n And reinforcing node H j Two parts. Is provided with n mapping nodes and j enhancement nodes, which are formed by independent component matrix S train Is mapped to obtain：Z i ＝φ(S train W ei +β ei ) Phi is a linear activation function, W ei Is a random weight, beta ei Is a random bias; mapping to obtain a mapping node Z n ＝(Z 1 ，Z 2 ，...，Z n ). Similarly, enhance node H j ＝(H 1 ，H 2 ，...，H j ) Is formed by mapping node Z n ＝(Z 1 ，Z 2 ，...，Z n ) The mapping is carried out to obtain: h j ＝ζ(Z n W hj +β hj ) ζ is an activation function tanh, W hj Is a random weight, beta hj Is a random bias; a is that n ＝[Z n |H j ]Is an extended input matrix formed by combining all mapping nodes and enhancement nodes; 100 mapping nodes and 100 enhancement nodes are determined as the number of network nodes during offline training; thus, the width learning model can be expressed as:
Y＝[φ(S train W e1 +β e1 )，...，φ(S train W en +β en )|ζ(Z n W h1 +β h1 )，...，ζ(Z n W hj +β hj )]W n
＝[Z 1 ，Z 2 ，...，Z n |H 1 ，...，H j ]W n
＝[Z n |H j ]W n
wherein ,
Figure QLYQS_5
W n the connection weight of the width system is obtained by using a ridge regression method, lambda is a constraint parameter, I is a unit matrix, A n ＝[Z n |H j ]Y is the label output obtained by training;
5) According to the steps, training the width learning model by utilizing different fault type data to obtain over-complete width model subsystems of C different fault types;
B. on-line monitoring:
6) Collecting current sewage treatmentProcess data X test As a test dataset;
7) Average and standard deviation vs. X of normal data obtained off-line test Standardized test data are obtained by standardized processing
Figure QLYQS_6
8) Calculation of
Figure QLYQS_7
Independent component matrix S of (2) test ：/>
Figure QLYQS_8
Wherein D is a unmixed matrix obtained in an off-line stage;
9) Matrix S of independent components test Respectively inputting the model into C overcomplete width model subsystems; in the C overcomplete width model subsystems, test data S is obtained by utilizing network parameters obtained through offline training test Mapping to characteristic nodes and enhancement nodes, and then utilizing a network weight matrix W obtained by offline training n For online data S test Reconstructing to obtain a reconstructed data tag
Figure QLYQS_9
Figure QLYQS_10
wherein ,
Figure QLYQS_11
Figure QLYQS_12
Figure QLYQS_14
；
10 Reconstructed data tag obtained by calculating C overcomplete width model subsystems
Figure QLYQS_15
If->
Figure QLYQS_16
If the value of (1) is 1, the fault is considered to occur, and an alarm is given; otherwise, it is normal.
2. The method for monitoring the fault of the sewage treatment process based on the overcomplete width learning model as claimed in claim 1, wherein the method comprises the following steps:
the standardized processing concretely comprises the following steps:
first, the mean and standard deviation of normal data are calculated, wherein the mean value at all times of the j-th variable is
Figure QLYQS_17
The standard deviation of all moments of the jth variable is +.>
Figure QLYQS_18
Figure QLYQS_19
Finally, normalized training data are obtained +.>
Figure QLYQS_20
3. The method for monitoring the fault of the sewage treatment process based on the overcomplete width learning model as claimed in claim 1, wherein the method comprises the following steps:
the number of network nodes in offline training is preferably 100 mapped nodes and 100 enhanced nodes.
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