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.
Highlights
Rolling bearings, as core components of rotating machinery, play a significant role in modern machines such as wind turbines, machine tools, centrifugal pumps, compressors, and motorized spindles
Aiming at the fault diagnosis of rolling bearings, an adaptive multiparameter-adjusting stochastic resonance (SR) method for bistable systems based on particle swarm optimization (PSO) algorithm has been proposed and studied in this paper. e tunable parameters include the traditional nonlinear system parameters and other generalized parameters such as the amplitude-transformation coefficient and the scale-transformation coefficient
With the output signal-to-noise ratio (SNR) (SNRout) as objective function, the optimal SR output of bistable systems can be obtained by adjusting the multiparameters adaptively for parameter-fixed vibration signals, and a larger SNR can be achieved in the output signal. us, potential weak rolling bearing fault features can be extracted using the proposed method and fault diagnosis can be realized
Summary
As core components of rotating machinery, play a significant role in modern machines such as wind turbines, machine tools, centrifugal pumps, compressors, and motorized spindles. In view of the good performance of SR using noise to enhance periodic signal features, SR-based weaksignal detection and fault diagnosis methods have been investigated and successfully applied in rolling bearing fault diagnosis [17, 18]. Erefore, the optimal output result may not be achieved especially for those signals with a low signal-to-noise ratio (SNR), limiting the detection performance of the adaptive SR methods.
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