Rolling Bearing Fault Diagnosis Based on Adaptive Multiparameter-Adjusting Bistable Stochastic Resonance

Abstract

The weak-signal detection technologies based on stochastic resonance (SR) play important roles in the vibration-based health monitoring and fault diagnosis of rolling bearings, especially at their early-fault stage. Aiming at the parameter-fixed vibration signals in practical engineering, it is feasible to diagnose the potential rolling bearing faults through adaptively adjusting the SR system parameters, as well as other generalized parameters such as the amplitude-transformation coefficient and scale-transformation coefficient. However, extant adaptive adjustment methods focus on the system parameters, while the adjustments of other adjustable parameters have not been fully studied, thus limiting the detection performance of the adaptive SR method. In order to further enhance the detection performance of adaptive SR methods and extend their application in rolling bearing fault diagnosis, an adaptive multiparameter-adjusting SR (AMPASR) method for bistable systems based on particle swarm optimization (PSO) algorithm is proposed in this paper. This method can produce optimal SR output through adaptively adjusting multiparameters, thus realizing fault feature extraction and further fault diagnosis. Furthermore, the influence of algorithm parameters on the optimization results is discussed, and the optimization results of the Langevin system and the Duffing system are compared. Finally, we propose a weak-signal detection method based on the AMPASR of the Duffing system and employ three diagnosis examples involving inner ring fault, outer ring fault, and rolling element fault diagnoses to demonstrate its feasibility in rolling bearing fault diagnosis.

Local image descriptor developed from Fechner’s law

“…In addition, more and more methods to improve LBP have emerged in recent years, such as local maximum edge cooccurance patterns, 12 local gradient patterns, 13 scale-adaptive local binary patterns, 14 local directional extrema number pattern (LDIRENP), 15 graph based structure binary pattern, 16 and other variants. [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] Ernst Weber, a 19th century experimental psychologist, observed that the ratio of incremental threshold to background intensity is a constant. This relationship is known as Weber's law and can be expressed as E Q -T A R G E T ; t e m p : i n t r a l i n k -; e 0 0 1 ; 1 1 6 ; 4 0 7…”

“…In addition, more and more methods to improve LBP have emerged in recent years, such as local maximum edge cooccurance patterns, 12 local gradient patterns, 13 scale-adaptive local binary patterns, 14 local directional extrema number pattern (LDIRENP), 15 graph based structure binary pattern, 16 and other variants 17 – 31 …”

Research on Fault Feature Extraction of Hydropower Units Based on Adaptive Stochastic Resonance and Fourier Decomposition Method

“…Unlike the conventional denosing methods, stochastic resonance (SR) is a method that reduces noise interference by transferring noise energy to fault signals. rough using a nonlinear system, part of the noise energy is transferred to the low-frequency signal, and the resonance of the weak signal submerged in the noise is strengthened so as to reduce the noise interference and extract the signal features effectively [30][31][32]. In order to solve the feature extraction and fault diagnosis of rolling bearings under a large amount of noise, Li and Shi [33] proposed a signal processing method that combined EEMD and adaptive stochastic resonance and applied it to the rolling bearing vibration signal, effectively enhancing the weak fault features and extracting them.…”

Multi-Frequency Weak Signal Decomposition and Reconstruction of Rolling Bearing Based on Adaptive Cascaded Stochastic Resonance

“…This method can effectively detect weak signal characteristics hidden in strong noise. Z. H. Lai et al [30] proposed an adaptive multi-parameter tuned stochastic resonance method for bistable systems based on particle swarm optimization algorithm, which generates optimal SR output by adaptively adjusting multiple parameters to achieve fault feature extraction and further fault diagnosis of rolling bearings. However, the cascaded bistable stochastic system involves multiple model parameters, the system parameters are difficult to adjust, the adjustment process is time-consuming, and engineers with insufficient experience may be unable to obtain the optimal SR output.…”