Rolling bearing fault diagnosis based on visual heterogeneous feature fusion

doi:10.11772/j.issn.1001-9081.2017.04.1207

Journal of Computer Applications ›› 2017, Vol. 37 ›› Issue (4): 1207-1211.DOI: 10.11772/j.issn.1001-9081.2017.04.1207

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Rolling bearing fault diagnosis based on visual heterogeneous feature fusion

YANG Hongbai^1,2, ZHANG Hongli², LIU Shulin²

1. School of Science and Technology, Shanghai Open University, Shanghai 200433, China;
2. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200072, China

Received:2016-09-19 Revised:2016-12-22 Online:2017-04-10 Published:2017-04-19
Supported by:
This work is partially supported by the National Natural Science Foundation of China (51575331).

基于可视化异类特征优选融合的滚动轴承故障诊断

杨洪柏^1,2, 张宏利², 刘树林²

1. 上海开放大学理工学院, 上海 200433;
2. 上海大学机电工程与自动化学院, 上海 200072

通讯作者: 杨洪柏
作者简介:杨洪柏(1972-),女,江苏沭阳人,副教授,博士,主要研究方向:计算机集成制造、故障诊断、信号处理;张宏利(1985-),男,山东临沂人,讲师,博士,主要研究方向:智能故障诊断、模式识别;刘树林(1963-),男,河北保定人,教授,博士,主要研究方向:复杂设备故障诊断。
基金资助:
国家自然科学基金资助项目（51575331）。

Abstract

Abstract: Aiming at the shortcomings of large feature set dimensionality, data redundancy and low fault recognition rate in existing fault diagnosis method based on simple combination of multi-classes features, a fault diagnosis method based on heterogeneous feature selection and fusion was proposed. The clustering characteristics of the feature data was analyzed according to the contours of the data of various class of features, and the redundant feature dimensions which are weakly clustered and not useful for fault classification were removed, only the feature dimensions with strong clustering characteristics were retained for the fault recognition. In the bearing fault diagnosis experiment, time-domain statistics and wavelet packet energy of fault signals were optimally selected and merged, and Back Propagation (BP) neural network was used for fault pattern recognition. The fault recognition rate reached 100%, which is significantly higher than that of the fault diagnosis method without feature selection and fusion. Experimental results show that the proposed method is easy to be implemented and can significantly improve the fault recognition rate.

Key words: heterogeneous feature, feature fusion, pattern recognition, fault diagnosis, rolling bearing

摘要： 针对故障诊断过程中基于简单的多类故障特征联合决策存在特征集维数多、数据冗余、故障识别率不高的缺点，提出了一种基于异类特征优选融合的故障诊断方法。该方法根据多类特征数据的轮廓图，分析各维特征数据的聚类特性，去除聚类性弱、对故障区分无益的冗余特征维度，仅保留聚类性强的特征维度用于故障识别。在轴承故障诊断实验中，选用故障信号时域统计量和小波包能量两类多维特征进行优选融合，并采用反向传播（BP）神经网络进行故障模式识别。故障识别率达到100%，显著高于无特征优选的故障诊断方法。实验结果表明所提出的方法简便易行，可以显著提高故障识别率。

关键词: 异类特征, 特征融合, 模式识别, 故障诊断, 滚动轴承

CLC Number:

TP391.4

YANG Hongbai, ZHANG Hongli, LIU Shulin. Rolling bearing fault diagnosis based on visual heterogeneous feature fusion[J]. Journal of Computer Applications, 2017, 37(4): 1207-1211.

杨洪柏, 张宏利, 刘树林. 基于可视化异类特征优选融合的滚动轴承故障诊断[J]. 计算机应用, 2017, 37(4): 1207-1211.

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Rolling bearing fault diagnosis based on visual heterogeneous feature fusion

基于可视化异类特征优选融合的滚动轴承故障诊断

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