LAUSR

Abstract：a#13; Incipient fault identification of rolling bearings is of great significance in avoiding the occurrence of malignant accidents in rotating machinery. However, the fault-related features at early stage are weak and easily contaminated by environmental noise, making them difficult to be identified 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 of rolling bearings. Firstly, in the BFD method, the vibration signal is adaptively decomposed into sparse narrow-band sub-signals in 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 decomposition results. Finally, the bearing faults are identified by matching the envelope spectrum with the defect frequency of theoretical calculation. To verify the validity of the proposed method, the 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 of rolling bearings. The superiority of the proposed BFD is also demonstrated by comparing with ensemble empirical mode decomposition (EEMD), variational mode decomposition (VMD) and an improved kurtogram method.a#13;