Weak Fault Feature Extraction for Rolling Element Bearing Based on a Two-Stage Method

IF 1.9 4区 计算机科学 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Lianhui Jia, Lijie Jiang, Yongliang Wen, Hongchao Wang
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引用次数: 0

Abstract

Timely and effective feature extraction is the key for fault diagnosis of rolling element bearing (REB). However, fault feature extraction will become very difficult in the early weak fault stage of REB due to the interference of strong background noise. To solve the above difficulty, a two-stage feature extraction method for early weak fault of REB is proposed, which mainly combines feature mode decomposition (FMD) with a blind deconvolution (BD) method. Firstly, based on the impulsiveness and cyclostationary characteristics of the vibration signal of faulty REB, FMD is used to decompose the complex original vibration signal into several modes containing single component. Subsequently, the sparse index (SI) is calculated for each mode, and the mode containing sensitive fault feature is selected for further analysis. Subsequently, apply the deconvolution method on the selected mode for further enhancing the impulsive characteristic. At last, traditional envelope spectrum (ES) analysis is applied on the filtered signal, and satisfactory fault features are extracted. Effectiveness and advantages of the proposed method are verified through experimental and engineering signals of REBs.
基于两阶段法的滚动轴承弱故障特征提取
及时有效的特征提取是滚动轴承故障诊断的关键。然而,由于强背景噪声的干扰,在REB的早期弱断层阶段,断层特征提取将变得非常困难。针对上述困难,提出了一种以特征模态分解(FMD)与盲反卷积(BD)相结合的REB早期弱故障两阶段特征提取方法。首先,基于故障REB振动信号的冲动性和周期平稳性特点,利用FMD将复杂的原始振动信号分解为包含单分量的多个模态;然后,计算每个模态的稀疏指数(SI),选择包含敏感故障特征的模态进行进一步分析。然后,对选择的模式进行反卷积,进一步增强脉冲特性。最后,对滤波后的信号进行传统的包络谱分析,提取出满意的故障特征。通过实验和工程信号验证了该方法的有效性和优越性。
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
CiteScore
6.50
自引率
4.30%
发文量
94
审稿时长
3.6 months
期刊介绍: International Journal of Distributed Sensor Networks (IJDSN) is a JCR ranked, peer-reviewed, open access journal that focuses on applied research and applications of sensor networks. The goal of this journal is to provide a forum for the publication of important research contributions in developing high performance computing solutions to problems arising from the complexities of these sensor network systems. Articles highlight advances in uses of sensor network systems for solving computational tasks in manufacturing, engineering and environmental systems.
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