Detection of Faults in Rotating Machinery Using Periodic Time-Frequency Sparsity

TL;DR

This paper introduces a novel sparse grid extraction method in the time-frequency domain for fault detection in rotating machinery, demonstrating improved detection of periodic oscillatory features in vibration signals.

Contribution

A new optimization-based approach using customized regularizers and an advanced algorithm for extracting periodic features in vibration signals for fault diagnosis.

Findings

Effective detection of periodic oscillatory features in simulated data.

Successful application to real bearing and gearbox fault diagnosis.

Outperforms some state-of-the-art methods in feature extraction.

Abstract

This paper addresses the problem of extracting periodic oscillatory features in vibration sig- nals for detecting faults in rotating machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain where the periodic oscillatory feature man- ifests itself as a relatively sparse grid. To estimate the sparse grid, we formulate an optimization problem using customized binary weights in the regularizer, where the weights are formulated to promote periodicity. In order to solve the proposed optimization problem, we develop an algorithm called augmented Lagrangian majorization-minimization algorithm, which combines the split augmented Lagrangian shrinkage algorithm (SALSA) with majorization-minimization (MM), and is guaranteed to converge for both convex and non-convex formulation. As examples, the proposed approach is applied to simulated data, and used as a tool for diagnosing faults in bearings and gearboxes for real data, and compared to some state-of-the-art methods. The results show the proposed approach can effectively detect and extract the periodical oscillatory features.