Compound Faults Diagnosis Method Based on Adaptive GST-NMF for Rolling Bearing

In order to solve the difficulty of features extraction of compound faults in underdetermined state, this research proposes an approach to extract signal features by combining adaptive generalized S transform (GST) and non-negative matrix factorization algorithm (NMF). The adaptive function (AF) is introduced to optimize GST. The optimized GST is used to process monitored signals to get the time-frequency features matrix. The NMF is improved by Itakura-Saito (IS) divergence. And the dimensionality of the signal time-frequency matrix is reduced by it. After iterative updating, several low-dimensional matrices are obtained. The time-domain waveforms of low-dimensional matrices are reconstructed, and the envelope spectrum analysis is performed to realize compound faults diagnosis. The simulation test and the actual bearing compound fault signals experiment prove that this method can effectively extract compound fault features in underdetermined state and realize bearing compound faults diagnosis.