Bandwidth Fourier decomposition and its application in incipient fault identification of rolling bearings

Abstract

The incipient fault identification of rolling bearings is of great significance in avoiding the occurrence of malignant accidents in rotating machinery. However, at early stages the fault-related features are weak and easily contaminated by environmental noise, making them difficult to identify by traditional methods. Hence, in this paper, a new optimized Fourier spectrum decomposition method, termed bandwidth Fourier decomposition (BFD), is proposed for early fault detection in rolling bearings. Firstly, in the BFD method, the vibration signal is adaptively decomposed into sparse narrow-band sub-signals in the frequency domain through bandwidth optimization. In order to improve the performance of spectrum decomposition, a new bandwidth estimation method and an improved variable initialization strategy are proposed on the basis of spectral energy distribution. Then, the obtained sub-signals are converted into time-domain bandwidth mode functions (BMFs) by inverse Fourier transform. After that, the fault characteristic frequency ratio (FCFR) is introduced to select the effective component from the decomposition results. Finally, the bearing faults are identified by matching the envelope spectrum with the defect frequency of the theoretical calculation. To verify the validity of the proposed method, simulation and experimental analysis are carried out in this paper. Preliminary results indicate that the proposed BFD can effectively enhance the recognition of incipient faults in rolling bearings. The superiority of the proposed BFD is also demonstrated by comparing it with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and an improved kurtogram method.