Fault Detection Based on Sliding Window and Multiblock Convolutional Autoencoders

doi:10.16182/j.issn1004731x.joss.22-1059

Abstract

Abstract:

In order to further improve the fault detection performance and fully mine the timing and hidden feature information, a fault detection method based on convolutional auto encoder is proposed. On the basis of modeling the original information set, the modeling of cumulative information and rate of change information is added to enhance the mining of implicit information; The three reconstructed information sets are sampled by sliding windows, and time series feature extraction and modeling are performed based on convolutional auto encoders.Bayesian fusion of the decision results of the convolutional auto encoder is performed to obtain the statistics, and the control limit is determined by the method of kernel density estimation for fault detection. The method is numerically simulated and applied in TE process, and the simulation results confirm the effectiveness and detection performance.

Key words: fault detection, convolutional autoencoder, multiblock modeling, sliding window, Bayesian fusion

CLC Number:

TP277

Mou Jianpeng, Xiong Weili. Fault Detection Based on Sliding Window and Multiblock Convolutional Autoencoders[J]. Journal of System Simulation, 2024, 36(2): 423-435.

Figures/Tables 15

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方法	误报率	报警率
CAE	0.005 0	0.662 5
SW-CAE	0.010 1	0.895 0
MBI-CAE	0.002 5	0.852 5
SW-MBI-CAE	0.005 1	0.942 5

故障编码	MBI-CAE				SW-MBI-CAE
故障编码	子块1	子块2	子块3	BIC	子块1	子块2	子块3	BIC
1	0.996 2	0.995 0	0.077 5	0.995 0	0.998 7	0.998 7	0.122 0	0.992 5
2	0.985 0	0.983 8	0.013 8	0.985 0	0.998 7	0.998 7	0.018 9	0.982 5
3	0.042 5	0.102 5	0.007 5	0.0737	0.035 2	0.135 8	0.007 5	0.041 3
4	0.972 5	1	0.018 7	1	1	1	0.020 1	1
5	0.255 0	0.882 5	0.071 2	0.828 7	0.254 1	0.932 1	0.084 3	0.833 7
6	1	0.998 8	0.051 2	1	1	1	0.054 1	1
7	1	0.998 8	0.187 5	1	1	1	0.236 5	1
8	0.975 0	0.978 8	0.423 8	0.977 5	0.978 6	0.986 2	0.636 5	0.977 5
9	0.035 0	0.072 5	0.025 0	0.063 8	0.055 3	0.117 0	0.039 0	0.055 0
10	0.450 0	0.600 0	0.095 0	0.611 2	0.508 2	0.667 9	0.150 9	0.625 0
11	0.683 7	0.920 0	0.077 5	0.938 7	0.836 5	0.988 7	0.168 6	0.975 0
12	0.985 0	0.996 2	0.783 7	0.995 0	0.997 5	1	0.953 5	1
13	0.947 5	0.953 7	0.561 3	0.950 0	0.950 9	0.957 1	0.704 4	0.947 5
14	1	0.778 3	0.998 8	1	1	1	1	0.998 8
15	0.071 2	0.170 0	0.015 0	0.135 0	0.088 1	0.223 9	0.039 0	0.165 0
16	0.253 7	0.440 0	0.106 2	0.430 0	0.281 8	0.517 0	0.140 9	0.452 5
17	0.900 0	0.976 2	0.287 5	0.976 2	0.947 2	0.979 9	0.359 7	0.975 0
18	0.9900 0	0.911 3	0.178 7	0.903 8	0.906 9	0.917 0	0.188 7	0.900 0
19	0.155 0	0.112 5	0.418 8	0.463 8	0.654 1	0.293 1	0.903 1	0.856 2
20	0.470 0	0.632 5	0.127 5	0.598 8	0.545 9	0.734 6	0.240 3	0.640 0
21	0.383 7	0.601 2	0.008 8	0.570 0	0.410 1	0.639 0	0.020 1	0.575 0
平均报警率	0.641 0	0.719 2	0.215 9	0.737 9	0.688 0	0.766 0	0.289 9	0.761 5
平均误报率	0.024 4	0.052 5	0.008 0	0.040 8	0.023 9	0.080 0	0.013 9	0.034 2

故障编码	LNSPPCAST^[28]	KECA^[29]	CAE	MBI-CAE	SW-MBI-CAE
平均报警率	0.703 5	0.795 3	0.789 1	0.845 8	0.874 0
1	0.996 9	0.997 5	0.992 5	0.995 0	0.992 5
2	0.988 1	0.981 3	0.982 5	0.985 0	0.982 5
4	0.068 1	1	1	1	1
5	0.513 1	0.246 3	0.258 7	0.828 7	0.833 7
6	0.981 3	1	1	1	1
7	0.251 9	1	1	1	1
8	0.981 2	0.973 8	0.972 5	0.9775	0.977 5
10	0.892 5	0.691 3	0.505 0	0.611 2	0.625 0
11	0.916 3	0.742 5	0.831 2	0.938 7	0.975 0
12	0.670 6	0.991 3	0.995 0	0.995 0	1
13	0.951 9	0.951 3	0.945 0	0.950 0	0.947 5
14	0.986 3	1	0.998 7	1	0.998 8
16	0.063 1	0.858 5	0.280 0	0.430 0	0.452 5
17	0.936 3	0.928 8	0.942 5	0.976 2	0.975 0
18	0.845 0	0.896 3	0.901 2	0.903 8	0.900 0
19	0.619 4	0.157 5	0.650 0	0.463 8	0.856 2
20	0.941 3	0.473 8	0.542 5	0.598 8	0.640 0
21	0.059 4	0.425 0	0.407 5	0.570 0	0.575 0

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