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

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

摘要：

针对炼化空压机故障诊断中故障机理复杂、先验知识欠缺，且传统浅层神经网络诊断精度不高等问题，结合深度置信网络（DBN）在特征提取和处理非线性数据等方面的优势，提出一种基于DBN的炼化空压机故障诊断方法。该方法利用空压机状态监测实测数据，实现训练网络的无监督特征学习和有监督微调，构建空压机故障的深层网络模型，从而实现对空压机故障类型的有效智能诊断。为评估该方法的有效性，与传统的故障诊断方法进行了比较研究，结果表明，该方法的诊断精度优于传统的故障诊断方法，且稳定性更好。

关键词: 空压机, 深度置信网络模型, 故障诊断, 稳定性

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.

鲁春燕, 李炜. 基于深度置信网络的炼化空压机故障诊断方法[J]. 化工学报, 2019, 70(2): 757-763.

Figures/Tables 8

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	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

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BP	72.83	2.7113	45.759
PNN	81.50	1.8649	30.536
DBN	94.59	0.4239	52.342

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