Bearing Compound Fault Diagnosis Using Energy-Constrained Swarm Decomposition and Adaptive Spectral Amplitude Modulation

In real industry, local damages often appear on multiple parts of bearings simultaneously and these compound faults coupling heavily troubles those regular diagnosis methods. In this article, an energy-constrained swarm decomposition and adaptive spectral amplitude modulation (ECSWD-ASAM) method is proposed to extract compound fault features from bearing vibration signals. First, a rectangular window is convolved with the energy spectrum to generate a smooth upper envelope curve to overcome the instant noisy peaks and highlight dominant frequencies in the vibration signals. Second, an energy filter is constructed to concentrate the energy of each oscillating component and suppress the spectrum overlap during the signal decomposition. Third, the envelope correlation coefficient (ECC) is used as the evaluation criterion to constrain the over-decomposition between adjacent components. Finally, a new index called kurtosis-enhanced entropy (KEE) is proposed to adaptively locate the optimal fault frequency band of the spectral amplitude modulation (SAM) results, which can enhance the weak fault features in the extracted components. ECSWD-ASAM effectively separates different fault components on simulated data, Paderborn University (PU) dataset, and vibration signals of bearing-gearbox testbed. The testing results indicate that ECSWD-ASAM has better denoising and compound fault extraction performance than these representative methods, i.e., successive variational mode decomposition (SVMD), improved complete ensemble empirical mode decomposition (EMD) adaptive noise (ICEEMDAN), and local mean decomposition (LMD).