Rolling element bearings are critical mechanical parts that are prone to damage, and the detection of their incipient faults plays an important role in ensuring the safe and reliable operation of rotating machinery. The incipient fault characteristics of rolling bearings suffer attenuation from complicated transmission paths and, moreover, are overwhelmed by background noise, hence it is a challenging task to extract them from a complex environment of vibration signals. In contrast to traditional signal processing methods, stochastic resonance (SR) methods can utilize the noise to highlight incipient fault characteristics. However, most overdamped SR methods can hardly suppress multiscale noise, and the monostable, bistable and even tristable SR methods can hardly achieve arbitrary stable-state matching between various mechanical vibration signals and stable-state types. Combined with genetic algorithms (GAs) and the fourth-order Runge-Kutta algorithm to simultaneously obtain the optimal system parameter, damping factor and damping factor of the new SR model, an improved underdamped periodic SR (UPSR) method with arbitrary stable-state matching in underdamped multistable nonlinear systems with a periodic potential for incipient bearing fault diagnosis is proposed. The periodic potential can achieve the matching between various vibration signals and arbitrary stable-state types and, moreover, underdamped SR can suppress the multiscale noise. To improve the performance in bearing fault detection, the signals in the actual engineering environment are preprocessed by prewhitening processing and a Hilbert transform. Therefore, the improved UPSR method is expected to possess a good ability for extracting incipient fault characteristics. Both simulated and experimental comparison with the underdamped bistable SR (UBSR) and fast-Kurtogram methods are adopted to verify the effectiveness of the proposed method. Compared with the above two methods, the proposed method has better fault characteristic frequency extraction performance. The results show that the proposed method could be more suitable and widely used for incipient bearing fault diagnosis in background noise.
High-precision spindle bearing is one of the most critical and vulnerable parts in a motorized spindle. Its unexpected failure may lead to production loss. Stochastic resonance (SR) is a weak signal detection method, which can obtain noise energy in strong background noise and enhance incipient fault characteristics of spindle bearing. Based on the fact that asymmetry can improve the enhancement ability of asymmetric bistable SR in weak feature extraction, we introduce an underdamped well-width asymmetric bistable SR (UABSR) method to the field of bearing fault diagnosis for the first time. However, the engineering application of UABSR can still be limited by two aspects. Firstly, the SNR index can take effect only when the actual fault frequency is obtained in advance, so the UABSR method is at high-cost in real practices. Secondly, an appropriate band-pass filter band range of the bearing faults can hardly be obtained due to the massive impulsive noise in operations. Here an improved UABSR method for spindle bearing fault diagnosis is proposed. Infogram method is used to process and analysis the original vibration signal for resisting the influence from the impulsive noise and obtaining more accurate frequency range of spindle bearing fault. In addition, time domain zero-crossing (TDZC) index, as the index of the improved UABSR method, can directly reflect the fault characteristics of spindle bearings without knowing the accurate fault characteristic frequency in advance. Besides, the Quantum Genetic Algorithms (QGAs) and the fourth-order Runge-Kutta algorithm are combined to simultaneously obtain the optimal system parameter, the asymmetric ratio, the damping factor and the rescaling factor of the improved UABSR model. Comparing the Infogram and original UABSR methods, the improved UABSR method performs better effect in incipient spindle bearing fault diagnosis. INDEX TERMS Spindle rolling bearing, fault diagnosis, underdamped asymmetric bistable stochastic resonance, vibration signal analysis, time domain zero-crossing index.
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