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Repetitive Transients Extraction Algorithm for Detecting Bearing Faults

机译：用于检测轴承故障的重复瞬态提取算法

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摘要

This paper addresses the problem of noise reduction with simultaneouscomponents extrac- tion in vibration signals for faults diagnosis of bearing.The observed vibration signal is modeled as a summation of two componentscontaminated by noise, and each component composes of repetitive transients. Toextract the two components simultaneously, an approach by solving anoptimization problem is proposed in this paper. The problem adopts convexsparsity-based regularization scheme for decomposition, and non-convexregularization is used to further promote the sparsity but preserving theglobal convexity. A synthetic example is presented to illustrate theperformance of the proposed approach for repetitive feature extraction. Theperformance and effectiveness of the proposed method are further demonstratedby applying to compound faults and single fault diagnosis of a locomotivebearing. The results show the proposed approach can effectively extract thefeatures of outer and inner race defects.
机译：本文解决了在振动信号中同时进行分量提取以减少轴承故障的降噪问题。将观察到的振动信号建模为两个被噪声污染的分量之和，每个分量都由重复的瞬变组成。为了同时提取这两个成分，提出了一种解决优化问题的方法。该问题采用基于凸稀疏性的正则化方案进行分解，并使用非凸正则化来进一步促进稀疏性但保留全局凸性。给出了一个综合的例子来说明所提出的方法用于重复特征提取的性能。通过将其应用于机车轴承的复合故障和单故障诊断，进一步证明了该方法的性能和有效性。结果表明，该方法可以有效地提取出内外圈缺陷的特征。

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