Enhanced Sparse Low-Rank Representation via Nonconvex Regularization for Rotating Machinery Early Fault Feature Extraction

The weak fault feature extraction is key to early fault diagnosis of rotary machinery. However, the existing sparse low-rank fault feature extraction methods have the deficiency of underestimation and low peak signal-to-noise ratio. To solve these problems, this article presents an enhanced sparse low-rank (ESL) representation approach for weak fault feature extraction. Considering the periodic self-similarity and shift invariance of fault feature, a weighted dual approximation regularization is proposed for noise and fault irrelevant harmonic suppression, which provides a cornerstone for rotating machinery weak fault feature extraction. To be specific, the truncated nuclear norm (TNN) and weighted generalized minimax-concave (WGMC) penalty are leveraged to form the weighted dual approximation regularization so that it can inherit their superior properties. The TNN can capture the periodic self-similarity and shift-invariance structure of fault impulses while restraining the noise; the WGMC penalty can enhance the sparsity, restrain the fault irrelevant harmonics, and overcome the deficiency of underestimating the large amplitude components. Therefore, the proposed model can effectively extract the weak fault feature. The proposed approach is applied to bearing and planet gear fault diagnosis to evaluate its effectiveness. Comparison results show the significant improvements of the proposed ESL method, indicating that it has great potentials in fault diagnosis of rotating machinery.

I. Introduction

Mechatronic systems are widely used, their safety and reliability are of primary importance. Predictive maintenance can detect potential faults and prevent deterioration. Fault feature extraction and diagnosis are important parts of predictive maintenance; they are effective means to maintain the safety and stability of rotating machinery. In some mechatronic systems of high-reliability requirements, such as aerospace [1], high-speed transport systems [2], manufacturing [3], wind turbine power generation [4]–[6], and other heavy industry applications, bearings and gears are the most easily damaged components, the failure of these two parts could result in safety hazards and property losses; thus, it is of great significance to accurately identify the incipient fault. The fault feature extraction and diagnosis of rotating machinery combine the dynamic characteristics of mechanical equipment, motor, sensors and measurement, signal acquisition, and processing technology organically, providing reliable reasoning for the diagnosis and maintenance of mechanical equipment.