Reciprocating Compressor Fault Diagnosis Technology Based on Multi-source Information Fusion

被引：8

作者：

Zhang M. ^{[1
]}

Jiang Z. ^{[1
]}

机构：

[1] Diagnosis and Self-Recovery Engineering Research Center, Beijing University of Chemical Technology, Beijing

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2017年 / 53卷 / 23期

关键词：

Fault diagnosis; Information fusion; RBF neural network; Reciprocating compressors; Weighted evidence theory multi-source;

D O I：

10.3901/JME.2017.23.046

中图分类号：

学科分类号：

摘要：

Due to the complex structure, various vibration excitation sources and closely fault correlation, different kinds of sensor information are needed to identify faults of reciprocating compressor. Based on fused diverse kinds of sensor acquired feature information of reciprocating compressors, a method for fault diagnosis of reciprocating compressors is proposed, and a fusion diagnosis framework is constructed. Evidence feature space is constructed by using multi-sensor information of reciprocating compressors, and initially diagnosed by using RBF neural network. According to weighted evidence theory, the final diagnosis is obtained by fusing diagnostic results of the RBF neural network. Three kinds working condition of the reciprocating compressor experimental data are diagnosed by the proposed method. Diagnosis result shows that diagnosis of multi-source information fusion has high reliability and low uncertainty. The proposed method can accurately identify the reciprocating compressor fault. © 2017 Journal of Mechanical Engineering.

引用

页码：46 / 52

页数：6

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