CIESC Journal ›› 2019, Vol. 70 ›› Issue (2): 757-763.doi: 10.11949/j.issn.0438-1157.20181357

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Fault diagnosis method of petrochemical air compressor based on deep belief network

Chunyan LU1,2,3(),Wei LI1,2,3()   

  1. 1. College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China
    2. Key Laboratory of Gansu Advanced Control for Industrial Processes, Lanzhou 730050, Gansu, China
    3. National Experimental Teaching Center of Electrical and Control Engineering, Lanzhou University of Technology, Lanzhou 730050, Gansu, China
  • Received:2018-11-16 Revised:2018-11-26 Online:2019-02-05 Published:2018-12-04
  • Contact: Wei LI E-mail:luchunyan@sina.com;liwei@lut.cn

Abstract:

According to the complexity of fault mechanism, the lack of prior knowledge, and the low diagnosis precision of traditional shallow layer neural network for the fault diagnosis of petrochemical air compressor, a kind of petrochemical air compressor fault diagnosis method is put forward based on the deep belief network because of its advantage in feature extraction and nonlinear data processing. By using state monitoring data of the air compressor, the method realizes the unsupervised characteristics learning and supervised fine-tuning of training network, constructs the deep network model of the air compressor fault, thus achieving the effective intelligent diagnosis for fault types of the air compressor. The effectiveness of the method is compared with the traditional fault diagnosis method. The results show that the diagnostic accuracy of the method is better than the traditional fault diagnosis method and the stability is better.

Key words: air compressor, deep belief network model, fault diagnosis, stability

CLC Number: 

  • TP273

Fig.1

Deep belief network architecture"

Fig.2

Restricted Boltzmann machine"

Fig.3

Flow chart of air compressor fault diagnosis"

Table 1

Description of air compressor fault datasets"

炼化空压机故障状况类型样本个数故障类别
正常28001
润滑系统故障28002
轴承故障28003
冷却水槽堵塞28004
级间转子不平衡28005

Fig.4

Diagnostic accuracy of different iterations"

Fig.5

Diagnostic accuracy of different hidden layers"

Fig.6

Diagnostic accuracy of different methods in 10 experiment"

Table 2

Diagnostic results with different methods"

方法平均准确率/%准确率标准差/%平均训练时间/s
多隐层BP72.832.711345.759
PNN81.501.864930.536
DBN94.590.423952.342
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