Bearing fault detection for drivetrains using adaptive filters based wavelet transform

Predicting a localized defect on a rolling bearing during the degradation process before a complete failure is crucial to prevent system failures, unscheduled downtimes and substantial loss of productivity. During this process, impulses associated with the fault are weak, nonstationary or time-frequency varying, and contaminated by noises, which render the problem of extracting these impulses very difficult. This work investigates the effectiveness of common signal processing techniques on predicting incipient faults, e.g. Fast Fourier transform, Short-Time Fourier transform, Wavelet transform. It was found that an adaptive filter is required to enhance and reconstruct the signals during th…