The fault detection and diagnosis in rolling element bearings using frequency band entropy

In vibration analysis, fault feature extraction from strong background noises is of great importance. Frequency band entropy based on short-time Fourier transform illustrates the complexity of every frequency component in the frequency domain, and it can be used to detect the periodical components hidden in the signal. This article shows how the frequency band entropy offers a robust way in detecting faults even when the signal is under strong masking noises. Furthermore, frequency band entropy provides a way of blindly designing optimal band-pass filters. The filtering signal combined with envelope analysis is helpful in fault diagnosis. The effectiveness of the proposed method is demonstrated on both simulated and actual data from rolling bearings.