Fault Detection of Rotation Machinery using Periodic Time-Frequency Sparsity

This paper addresses the problem of extracting periodic oscillatory features in vibration signals for detecting faults in rotation machinery. To extract the feature, we propose an approach in the short-time Fourier transform (STFT) domain, where the periodic oscillatory feature manifests 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. 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 to some state-of-the-art methods. The results show the proposed approach can effectively detect and extract the periodical oscillatory features.