基于SPWVD识别的滚动轴承智能检测方法

林勇;周晓军;杨先勇;张文斌

振动与冲击 ›› 2009, Vol. 28 ›› Issue (9) : 86-90.

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PDF(868 KB)
振动与冲击 ›› 2009, Vol. 28 ›› Issue (9) : 86-90.
论文

基于SPWVD识别的滚动轴承智能检测方法

  • 林勇,周晓军,杨先勇,张文斌
作者信息 +

Intelligent fault diagnosis methods of bearing based on SPWVD and AIN

  • LIN Yong,ZHOU Xiaojun,YANG Xianyong,ZHANG Wenbin
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文章历史 +

摘要

为了探索基于振动谱图像模式识别的智能故障检测方法,以滚动轴承为对象,提出了用SPWVD分布来表征振动信号时频分布特性,利用SPWVD图像的GLCM及其特征统计量来提取故障特征。改进了人工免疫网络分类算法,通过人工免疫网络分类方法对故障样本特征统计量进行学习,形成记忆抗体集,进而对检验抗原进行故障分类识别,在故障特征信号干扰严重的情况下,取得了较BP神经网络好的检测准确率,验证了人工免疫网络良好的适应性。随着智能故障检测技术发展,基于图像模式识别的故障检测方法必将得到推广和应用,本文验证其在轴承故障监测中的可行性。

Abstract

This paper is devoted to exploring intelligent fault diagnosis methods which is based on pattern recognition of vibration spectrogram. Firstly, taking rolling bearing as an example, the GLCM extracted from SPWVD spectrogram and its characteristic statistic are described. Moreover a modified AIN algorithm is introduced and used in bearing fault diagnosis. Through the optimization of fault antigen sample,the memory antibodies sets are formed and classification is processed by the k-nearest neighbor method. A mass of fault sample are analyzed in the algorithm proposed and the results are compared with those obtained by BPNN. The comparison result indicates that the modified AIN algorithm has better classification ability as well as high diagnosis accuracy. As the intelligent fault diagnosis methods develop,methods based on spectrogram identification should be popularized,its practicability is proved through recognition of bearing fault in this paper.

关键词

威格纳-维尔分布 / 灰度共生矩阵 / 人工免疫网络 / 智能故障检测 / 滚动轴承

Key words

Wigner-Ville distribution / gray level co-occurrence matrix / artificial immune network / intelligent fault diagnosis / rolling bearing

引用本文

导出引用
林勇;周晓军;杨先勇;张文斌. 基于SPWVD识别的滚动轴承智能检测方法 [J]. 振动与冲击, 2009, 28(9): 86-90
LIN Yong;ZHOU Xiaojun;YANG Xianyong;ZHANG Wenbin . Intelligent fault diagnosis methods of bearing based on SPWVD and AIN[J]. Journal of Vibration and Shock, 2009, 28(9): 86-90
中图分类号: TP277   

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