CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 736-749.doi: 10.11949/j.issn.0438-1157.20180842

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Fault diagnosis based on PCA method with multi-block information extraction

Bingbin GU1(),Weili XIONG2()   

  1. 1. School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, Jiangsu, China
    2. Key Laboratory of Advanced Process Control for Light Industry, Jiangnan University, Wuxi 214122, Jiangsu, China
  • Received:2018-07-21 Revised:2018-11-22 Online:2019-02-05 Published:2018-12-04
  • Contact: Weili XIONG E-mail:18861822198@163.com;greenpre@163.com

Abstract:

Traditional monitoring methods only use sensor observation information to perform process fault monitoring, while ignoring other valid information contained in the original data. Aiming to this problem, a PCA fault monitoring algorithm based on multi-block information extraction is proposed. Firstly, two kinds of information of the cumulative error and the change rate of process variables are defined, so that new feature information can be extracted from the data. The process is divided into three sub-blocks based on each feature, and each sub-block is processed by the PCA method. Modeling and monitoring are carried out, and monitoring results are integrated by Bayesian method. Finally, a fault diagnosis method with weighted contribution graph is proposed to find the source variable which causes the fault. The validity and feasibility of the proposed method are demonstrated by numerical examples and the application of Tennessee-Eastman (TE) process monitoring.

Key words: principal component analysis, algorithm, model, fault diagnosis, information extraction, multi-block modelling

CLC Number: 

  • TP277

Fig.1

Comparison between traditional multi-block modeling method and multi-block information modeling method"

Table 1

Sub-block division results"

子块编号训练数据集测试样本
1Xxtest
2XIxtestI
3XDxtestD

Fig.2

Fault monitoring flowchart based on MBI-PCA"

Fig.3

Monitoring result of fault 1"

Fig.4

Isolation result of fault 1"

Fig.5

Monitoring result of fault 2"

Fig.6

Isolation result of fault 2"

Table 2

Missing alarm rates of TE process/%"

故障编号子块1子块2子块3BIC
T2SPET2SPET2SPET2SPE
10.880.131.250.3889.7483.230.880.25
21.634.261.63297.8797.371.42.38
399.1297.3797.7596.2598.2596.6299.2596.25
479.1022.4098.597.8736.670
575.7279.166.460.1391.8687.9870.460
60.8801.130.1397.1284.6110
7000.130.1379.676.9700
83.1316.43.382.1359.9534.673.381.38
998.2598.2598.1293.9997.6296.599.1294.99
1070.0974.2259.262.3392.3784.7362.3360.08
1159.3225.1633.544.3893.4993.3731.663.49
121.6310.512.380.8826.7820.651.131
136.384.765.884.6345.5628.295.884.41
140.75074.4710.760.131.3800
1598.629796.3796.3798.379798.2595.12
1686.6172.5979.7262.3388.3684.6178.660.45
1723.534.6311.392.6389.1157.5712.392.33
1810.769.8910.519.5183.7378.3511.019.41
1988.9987.6188.8688.1174.7245.0675.3444.18
2068.3450.3166.9632.7986.9889.6164.5836.47
2160.8352.8254.6931.1698.6298.7558.3234.67
平均故障漏报率44.5037.3841.7228.2680.4273.1038.6526.12
平均故障误报率0.521.320.83.351.382.670.373.07

Fig.7

Monitoring result of fault 5"

Fig.8

Isolation result and related variable curves of fault 5"

Fig.9

Monitoring result of fault 11"

Fig.10

Isolation result and related variable curves of fault 11"

Fig.11

Comparison between PCA and MBI-PCA of fault 21"

Fig.12

Monitoring results of fault 0"

Table 3

Comparison of some state of multi-block monitoring methods"

故障编号PCADPCAMSMBPCAFBPCAMBI-PCA
T2 or SPEBICT2 or SPEBICT2 or SPEBICT2 or SPEBICT2 or SPE
00.0220.0260.02140.060.0144
100000
20.020.020.010.010.01
30.970.940.880.96
400000
50.750000
600000
700000
80.030.020.030.010.01
90.980.930.910.95
100.70.440.180.110.6
110.250.220.30.190.03
120.020.010.020.010.01
130.050.050.050.050.05
1400000
150.970.930.780.95
160.730.520.150.10.30
170.050.030.130.030.03
180.100.090.10.10.09
190.880.80.480.30.44
200.500.270.330.10.36
210.530.440.460.330.35
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