基于量子遗传算法优化RVM的滚动轴承智能故障诊断

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振动与冲击 ›› 2015, Vol. 34 ›› Issue (17) : 207-212.

论文

王波1,2 刘树林1 蒋超1 张宏利1

作者信息 +

Rolling bearing intelligent fault diagnosis based on RVM optimized by Quantum genetic algorithm

WANG Bo1,2 LIU Shu-lin1 JIANG Chao1 ZHANG Hong-li1

Author information +

文章历史 +

摘要

提出了基于量子遗传算法(QGA)优化相关向量机(RVM)核函数参数的方法，通过仿真比较了量子遗传算法与其它方法在核函数参数优化方面的性能，结果表明基于量子遗传算法优化出的算法性能优于其它方法的优化性能。将基于量子遗传算法优化的相关向量机(QGA-RVM)应用于滚动轴承的故障诊断；采用总体平均经验模态分解(EEMD)将滚动轴承故障信号自适应地分解成多个内禀模态函数(IMF)，将IMF能量作为故障特征输入到QGA-RVM进行最终的故障诊断。结果表明，该方法能够快速准确地诊断出滚动轴承故障，验证了该方法的有效性和稳定性；此外，通过与支持向量机(SVM)的对比分析，显示了RVM在智能故障诊断应用中的优越性。

Abstract

A novel method to optimize Relevance Vector Machine (RVM) kernel function parameters based on Quantum Genetic Algorithm (QGA) was proposed. It was compared with other optimization algorithms by simulation experiments, and the results showed that the optimization method based on QGA is superior to other optimization methods. The model of RVM optimized by QGA (QGA-RVM) was applied to fault diagnosis of rolling bearing. Fault signals were decomposed adaptively into some intrinsic mode functions (IMFs) by ensemble empirical mode decomposition (EEMD), and the IMF energies, as fault features, were inputted into QGA-RVM for final fault diagnosis. Experimental results showed that the proposed method can diagnose faults rapidly and accurately, and they also demonstrated the validity and stability. Moreover, the comparison with Support Vector Machine (SVM) showed the superiority of RVM in intelligent fault diagnosis.

导出引用

王波1,2 刘树林1 蒋超1 张宏利1 . 基于量子遗传算法优化RVM的滚动轴承智能故障诊断[J]. 振动与冲击, 2015, 34(17): 207-212

WANG Bo1,2 LIU Shu-lin1 JIANG Chao1 ZHANG Hong-li1. Rolling bearing intelligent fault diagnosis based on RVM optimized by Quantum genetic algorithm[J]. Journal of Vibration and Shock, 2015, 34(17): 207-212

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