A Sparse Representation Method Based on Multiobjective Optimization for the Extraction of Nonperiodic Fault Features of Rolling Bearing Under Variable Speed

IF 4.3 2区综合性期刊 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Sensors Journal Pub Date : 2024-12-30 DOI:10.1109/JSEN.2024.3520504

Keshen Cai;Chunlin Zhang;Yanfeng Wang;Zicheng Lu;Xingcai Wang;Zhe Meng

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引用次数: 0

Abstract

Fault signature extraction of rolling bearings under variable speed conditions is crucial while still a challenge due to the nonperiodic features of the fault impulses. A sparse representation method based on multiobjective optimization (MOO) is proposed to extract the nonperiodic fault impulses with high fidelity, in which the sparse representation with the generalized minimax concave (GMC) regularization based on flexible analytic wavelet transform (WT) enhanced is adopted. Moreover, a MOO model based on the angular domain correlated kurtosis (AD-CK) and the harmonic-to-noise ratio of envelope order spectrum (HNR-EOS) is constructed for adaptive parameters optimization of the sparse representation model, upon which a density estimation strategy is proposed to determine the optimal parameters from the Pareto front originally obtained via NSGA-II algorithm. The nonperiodic fault impulses can thus be extracted with the fault signature further identified from the envelope order spectrum. The method is validated by analyzing simulation and experiment signals.

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基于多目标优化的稀疏表示方法在变转速下滚动轴承非周期故障特征提取中的应用

变速条件下滚动轴承故障特征的提取至关重要，但由于故障脉冲的非周期性，仍然是一个挑战。提出了一种基于多目标优化（MOO）的高保真非周期故障脉冲稀疏表示方法，该方法采用基于增强柔性解析小波变换（WT）的广义极大极小凹（GMC）正则化稀疏表示。构建了基于角域相关峰度（AD-CK）和包络阶谱谐波噪比（HNR-EOS）的MOO模型，对稀疏表示模型进行自适应参数优化，并在此基础上提出了密度估计策略，从NSGA-II算法获得的Pareto前沿确定最优参数。通过进一步从包络阶谱中识别出故障特征，可以提取出非周期故障脉冲。通过对仿真和实验信号的分析，验证了该方法的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Sensors Journal 工程技术-工程：电子与电气

CiteScore

7.70

自引率

14.00%

发文量

2058

审稿时长

5.2 months

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