Enhanced Frequency Band Entropy Method for Fault Feature Extraction of Rolling Element Bearings

Frequency band entropy (FBE) has been proved usable in the fault diagnosis of rolling bearings, but its performance is poor in the presence of non-Gaussian noise and a low signal-to-noise ratio. In order to extract the transient impulsive signals more effectively, wavelet packet transform (WPT) is considered as an alternative method for signal decomposition. Therefore, by introducing WPT into FBE, this article introduces an enhanced FBE (EFBE) adopting WPT as the filter of FBE to overcome the shortcomings of the original FBE. Then, the depth of EFBE is optimized using adaptive resonance bandwidth and power amplitude spectrum entropy (PASE). Third, a novel method based on the indicator PASE is introduced to select the optimal node of EFBE. Finally, the filtered signal is combined with the envelope power spectrum to extract the fault feature frequency. In addition, an evaluation indicator is proposed to evaluate the performance of the EFBE. The simulation and cases are used to demonstrate the effectiveness and improved performance of the EFBE compared with the original FBE and other typical methods. The results show that the EFBE can detect various rolling bearing failures and implement its fault diagnosis effectively.

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