Yao Renhe, Jiang Hongkai, Wu Zhenghong, Wang Kaibo
School of Civil Aviation, Northwestern Polytechnical University, 710072 Xi'an, China.
School of Civil Aviation, Northwestern Polytechnical University, 710072 Xi'an, China.
ISA Trans. 2021 Dec;118:219-237. doi: 10.1016/j.isatra.2021.02.023. Epub 2021 Feb 19.
Incipient fault detection of rolling bearings is a challenging task since the weak fault features are disturbed by heavy background noise. This paper develops a periodicity-enhanced sparse representation method to address this issue. Firstly, periodicity-enhanced basis pursuit denoising (PBPD) is proposed by the theoretical derivation. Fault proportion is defined to quantify the single fault severity of sparse signals, then a periodicity-decision criterion for determining the optimal potential fault period is designed to periodically filter the last sparse signal. Secondly, the suitable linear transformation for PBPD is investigated in comparison and maximal overlapping discrete wavelet packet transform is adopted eventually. Thirdly, adaptive selection strategies are developed for the key parameters of PBPD. Simulations and experimental verifications demonstrate PBPD's excellent performance in rolling bearing incipient fault detection.
滚动轴承的早期故障检测是一项具有挑战性的任务，因为微弱的故障特征会受到强烈背景噪声的干扰。本文提出了一种周期性增强的稀疏表示方法来解决这一问题。首先，通过理论推导提出了周期性增强基追踪去噪（PBPD）方法。定义故障比例以量化稀疏信号的单个故障严重程度，然后设计一个用于确定最优潜在故障周期的周期性判定准则，对最后一个稀疏信号进行周期性滤波。其次，通过比较研究了适用于PBPD的线性变换，最终采用了最大重叠离散小波包变换。第三，针对PBPD的关键参数制定了自适应选择策略。仿真和实验验证表明PBPD在滚动轴承早期故障检测中具有优异的性能。
