Zero-Phase Filter-Based Adaptive Fourier Decomposition and Its Application to Fault Diagnosis of Rolling Bearing

Empirical wavelet transform (EWT) is a recently developed non-stationary signal decomposition approach. However, the number of spectrum divisions needs to be preset first, and the performance of spectrum division is inferior for a strong noisy or non-stationary signal caused by the mode mixing problem. To address this issue, a novel non-stationary signal decomposition method termed zero-phase filter-based adaptive Fourier decomposition (ZPF-AFD) is proposed in this paper. In the ZPF-AFD method, the number of spectrum divisions is adaptively determined first using the envelope entropy (EE) metric. Next, the spectral envelope processing (SEP) is applied to achieve an adaptive spectrum division. Last, the zero-phase filter (ZPF) is utilized to filter the frequency domain signal to obtain the components. The ZPF can effectively eliminate the mode mixing problem of EWT because of its no transition phase. The proposed ZPF-AFD approach is contrasted with the existing EMD, VMD, and EWT approaches through analyzing the simulated and measured signals in rolling bearing with local failures. The experimental results demonstrate that the proposed ZPF-AFD method has the best anti-noise and diagnosis accuracy, and its diagnosis performance is superior to the compared methods.