基于深度置信网络的炼化空压机故障诊断方法

doi:10.11949/j.issn.0438-1157.20181357

Abstract

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

Chunyan LU, Wei LI. Fault diagnosis method of petrochemical air compressor based on deep belief network[J].CIESC Journal, 2019, 70(2): 757-763.

Figures/Tables 8

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炼化空压机故障状况类型	样本个数	故障类别
正常	2800	1
润滑系统故障	2800	2
轴承故障	2800	3
冷却水槽堵塞	2800	4
级间转子不平衡	2800	5

方法	平均准确率/%	准确率标准差/%	平均训练时间/s
多隐层BP	72.83	2.7113	45.759
PNN	81.50	1.8649	30.536
DBN	94.59	0.4239	52.342

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

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