Rolling Bearing Fault Diagnosis Based on Nonlinear Underdetermined Blind Source Separation

One challenge of bearing fault diagnosis is that the vibration signals are often a nonlinear mixture of unknown source signals. In addition, the practical installation position also limits the number of observed signals. Hence, bearing fault diagnosis is a nonlinear underdetermined blind source separation (UBSS) problem. In this paper, a novel nonlinear UBSS solution based on source number estimation and improved sparse component analysis (SCA) is proposed. Firstly, the ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), and adaptive threshold singular value decomposition (ATSVD) joint approach is proposed to estimate the source number. Then, the observed signals are transformed into the time-frequency domain by short-time Fourier transform (STFT) to meet the sparsity requirement of SCA. The frequency energy is adopted to increase the accuracy of fuzzy C-means (FCM) clustering, so as to ensure the accuracy estimation of the mixing matrix. The L1-norm minimization is utilized to recover the source signals. Simulation results prove that the proposed UBSS solution can exactly estimate the source number and effectively separate the simulated signals in both linear and nonlinear mixed cases. Finally, bearing fault testbed experiments are conducted to verify the validity of the proposed approach in bearing fault diagnosis.

Automated summary

This paper proposes a novel nonlinear underdetermined blind source separation (UBSS) solution for bearing fault diagnosis. It utilizes source number estimation and improved sparse component analysis (SCA) to deal with the problem of nonlinear mixture of vibration signals. The proposed approach includes ensemble empirical mode decomposition (EEMD), correlation coefficient (CC), and adaptive threshold singular value decomposition (ATSVD) for source number estimation, and short-time Fourier transform (STFT) for transforming observed signals into the time-frequency domain. The results from simulations and experiments demonstrate that the proposed UBSS solution can accurately estimate the source number and effectively separate the signals.