Bearing fault feature extraction method: stochastic resonance-based negative entropy of square envelope spectrum

The early identification of bearing defects has recently attracted increasing attention in the fields of condition monitoring and predictive maintenance because of the critical role of bearings on the reliability and safety of turbomachines. The weak features representing early faults in the vibration signals are often submerged in the environmental noise, which poses a major challenge for the early fault diagnosis of rolling bearings. This study proposes a negative entropy of the square envelope spectrum approach integrated with optimized stochastic resonance (SR)-based signal enhancement for accurate early defect detection of rolling element bearings. The proposed method considers the cyclostationarity and impulsivity of the raw signal, as well as its similarity with the enhanced signal, thus reinforcing the characteristic frequency while integrating the regularity of the raw signal to evaluate the SR performance. A comparison study with different existing methods using both numerical and experimental data was conducted to illustrate the effectiveness and accuracy of the proposed methodology for early defect detection of rolling element bearings in different locations. The results show that the proposed method improves the fault detection by 3.5 d earlier than other SR methods, and produces the best enhancement results for fault detection in the outer race, inner race, and rolling element of bearings, with the increase of characteristic frequency intensity coefficient by 126.3%, 118.1%, and 100.5% compared to traditional envelope signals, respectively.