An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis

Cheng, Wei; Xu, Xuemei; Ding, Yipeng; Sun, Kehui; Li, QuanQuan; Dong, Lirong

doi:10.1016/j.cjph.2020.03.015

Cited by 15 publications

(3 citation statements)

References 38 publications

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“…Li et al [22] proposed a method based on unsaturated piecewise linear SR to realize more advantageous early bearing fault diagnosis by independently adjusting the barrier height and potential wall inclination. Cheng et al [23] studied an adaptive smooth unsaturated bistable SR system for bearing fault detection, which effectively suppressed the interference of low-frequency and high-frequency components. In addition, Zhang et al [24] found in the study of asymmetric system that after adding asymmetric factor, the system performance shows better advantages than symmetric system with the change of symmetry of potential well structure.…”

Section: Introductionmentioning

confidence: 99%

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

Zhang

Zhu

zhang

2022

Meas. Sci. Technol.

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Early fault diagnosis of rolling bearings is of great significance in the application of mechanical equipment, which makes the extraction of weak fault signals particularly critical by stochastic resonance (SR). Compared with bistable SR, tristable SR has stronger advantages in weak signal extraction, but the classical tristable system (CTSR) was limited by output saturation, which resulted in insufficient signal amplification ability. To solve the above problems, combined with asymmetric system whose output can be improved to a higher degree, a novel piecewise unsaturated asymmetric tristable stochastic resonance (NPUATSR) system is proposed. Through numerical simulation, it is concluded that NPUATSR output amplitude varies proportionally with the amplitude of the input, which overcomes the output saturation of CTSR. Secondly, the stationary probability density and mean first passage time of particles are derived by using adiabatic approximation theory, and the variation law caused by parameters is analyzed in combination with potential function, the internal mechanism of the system is further studied. Through the output signal-to-noise ratio (SNR), it is found that the performance advantage of NPUTASR system is the most obvious, and different parameters affect the output SNR. Finally, the adaptive genetic algorithm is used to optimize the parameters, and the proposed system is applied to early fault diagnosis on different types of bearings. After comparison with different systems, the results show that NPUATSR can effectively detect the fault frequency, and has the most outstanding advantages in spectrum amplification and anti-noise performance, which proves that NPUATSR system has significant value in practical engineering application.

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Section: Introductionmentioning

confidence: 99%

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

Zhang

Zhu

zhang

2022

Meas. Sci. Technol.

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“…Qiao et al 14 established a piecewise bistable potential model without output saturation to improve the output SNR. Cheng et al 15 demonstrated that smooth unsaturated bistable stochastic resonance can effectively suppress the interference of low‐frequency and high‐frequency. Wang et al 16 proposed an adaptive piecewise hybrid stochastic resonance and defined a novel parameter to improve the accuracy of characteristic frequency.…”

Section: Introductionmentioning

confidence: 99%

An adaptive improved unsaturated bistable stochastic resonance method based on weak signal detection

Cui

Yang

Wang

et al. 2021

Adaptive Control & Signal

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Stochastic resonance can detect weak periodic signals from strong background noise without loss of signal energy. However, the classical bistable stochastic resonance has the inherent output saturation defect, which limits the detection performance of system. And it is more difficult to detect signal with strong background noise. In this article, we constructed improved unsaturated bistable stochastic resonance to overcome this shortcoming. The improved bistable potential function makes the output signal more easily oscillate in two potential wells. To improve the stability and the accuracy of the method, we further propose an adaptive improved unsaturated bistable stochastic resonance (AIUBSR) by constructing a synthetic index (SI). The SI combines zero‐crossing ratio and structural correlation coefficient, which can measure the periodicity of output signal and the accuracy of detective frequency at the same time. Theoretical analysis and numerical simulations show that the proposed AIUBSR can have good weak signal detection capability in strong background noise.

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“…According to the limitations of the above methods, Zhang and Zhou [19] proposed the ensemble empirical mode decomposition method, which introduced noise to deal with the mode aliasing problem, so as to realize the fault feature extraction. Noise acts as a kind of energy signal, and the stochastic resonance method improves the detection performance of weak faults by converting noise energy into weak signals and then realizes fault feature extraction of bearings [20,21].…”

Section: Introductionmentioning

confidence: 99%

Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second‐Order Stochastic Resonance Method with Barrier Height Optimization

Shi

Zhou

et al. 2021

Shock and Vibration

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The stochastic resonance (SR) method is widely applied to fault feature extraction of rotary machines, which is capable of improving the weak fault detection performance by energy transformation through the potential well function. The potential well functions are mostly set fixed to reduce computational complexity, and the SR methods with fixed potential well parameters have better performances in stable working conditions. When the fault frequency changes in variable working conditions, the signal processing effect becomes different with fixed parameters, leading to errors in fault detection. In this paper, an underdamped second-order adaptive general variable-scale stochastic resonance (USAGVSR) method with potential well parameters’ optimization is put forward. For input signals with different fault frequencies, the potential well parameters related to the barrier height are figured out and optimized through the ant colony algorithm. On this basis, further optimization is carried out on undamped factor and step size for better fault detection performance. Cases with diverse fault types and in different working conditions are studied, and the performance of the proposed method is validated through experiments. The results testify that this method has better performances of weak fault feature extraction and can accurately identify different fault types in the input signals. The method proves to be effective in the weak fault extraction and classification and has a good application prospect in rolling bearings’ fault feature recognition.

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An adaptive smooth unsaturated bistable stochastic resonance system and its application in rolling bearing fault diagnosis

Cited by 15 publications

References 38 publications

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

Research and application of a novel piecewise unsaturated asymmetric tristable stochastic resonance system

An adaptive improved unsaturated bistable stochastic resonance method based on weak signal detection

Weak Fault Detection for Rolling Bearings in Varying Working Conditions through the Second‐Order Stochastic Resonance Method with Barrier Height Optimization

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