Bearing Fault Diagnosis Using Reconstruction Adaptive Determinate Stationary Subspace Filtering and Enhanced Third-Order Spectrum

Raw vibration signals poorly perform in industrial bearing fault diagnosis because impulse features are damped and masked by disturbances and noises. Fault diagnosis is more challenging due to weak features. This work presents a signal filtering and fault characteristic enhancement method based on reconstruction adaptive determinate stationary subspace filtering (Rad-SSF) and enhanced third-order spectrum to address the above problems. In particular, Rad-SSF reconstructs an adaptive self-determined, decomposed vibration signal trajectory matrix to obtain non-stationary signals. Then, the filtered signal with the best fault characteristics is extracted according to kurtosis. A 1.5-dimensional third-order energy spectrum is performed to enhance the fault characteristics by strengthening the fundamental frequency and eliminating non-coupling harmonics. Finally, the dominant frequency in the spectrum is contrasted to recognize fault diagnosis, referring to theoretical fault characteristic frequency. The feasibility and effectiveness of the proposed method are demonstrated by simulation and engineering signals under different conditions.