基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法

熊庆,张卫华

振动与冲击 ›› 2015, Vol. 34 ›› Issue (11) : 188-193.

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PDF(2844 KB)
振动与冲击 ›› 2015, Vol. 34 ›› Issue (11) : 188-193.
论文

基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法

  • 熊庆 ,张卫华
作者信息 +

Rolling Bearing Fault Diagnosis Method Using MF-DFA and LSSVM Based on PSO

  • Xiong Qing , Zhang Weihua
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文章历史 +

摘要

针对滚动轴承故障损伤程度难以确定的问题,提出对滚动轴承不同故障位置、不同损伤程度的振动信号进行故障特征提取及智能分类的故障诊断方法。先对各状态振动信号进行MF-DFA分析,选取敏感性及稳定性最好的二种多重分形谱参数作为故障特征量,然后输入到经过PSO参数优化的LSSVM中进行故障诊断。通过仿真试验、应用实例验证了该方法的有效性,并与LSSVM、SVM方法的诊断结果进行比较。结果表明:所提方法可实现滚动轴承故障位置及损伤程度的智能诊断,比直接LSSVM、SVM方法具有更优的泛化性,适合解决实际工程问题。

Abstract

When the rolling bearing fails, it is usually difficult to determine the damage degree. Aiming at this problem, a new fault diagnosis method was presented to achieve feature extraction and intelligent classification of different fault positions and damage degree of rolling bearing signal. To start with, MF-DFA was used to compute the multi-fractal spectrum of the vibration signal of each status.Next, two kinds of multi-fractal spectrum parameters which are the most sensitive and stable were found and employed as fault feature values. Then feature values were regarded as the input of LSSVM based on PSO for judging rolling bearing fault position and its damage degree. Finally, the effectiveness of the method was verified by simulation testing and applicable example, and comparison was made with other related methods. The results show that, the presented method can accurately achieved the intelligent diagnosis of rolling bearing fault position and damage degree, has better generalization than direct LSSVM or SVM method, and is suitable for solving practical engineering problems.

关键词

滚动轴承 / 故障诊断 / 多重分形去趋势波动分析 / 粒子群优化算法 / 最小二乘支持向量机

Key words

rolling bearing / faults diagnosis / multi-fractal detrended fluctuation analysis / particle swarm   optimization algorithm / least squares support vector machine

引用本文

导出引用
熊庆,张卫华. 基于MF-DFA与PSO优化LSSVM的滚动轴承故障诊断方法[J]. 振动与冲击, 2015, 34(11): 188-193
Xiong Qing,Zhang Weihua. Rolling Bearing Fault Diagnosis Method Using MF-DFA and LSSVM Based on PSO[J]. Journal of Vibration and Shock, 2015, 34(11): 188-193

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