Detection of Faults in Rotating Machinery Using Periodic Time-Frequency Sparsity
This addresses fault detection in rotating machinery like bearings and gearboxes, but it appears incremental as it builds on existing STFT and optimization techniques.
The paper tackled the problem of detecting faults in rotating machinery by extracting periodic oscillatory features from vibration signals, proposing a method that uses customized binary weights in the STFT domain to promote periodicity and showing it can effectively detect and extract these features compared to state-of-the-art methods.
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.