Abstract
Stochastic resonance (SR), a typical randomness-assisted signal processing method, has been extensively studied in bearing fault diagnosis to enhance the feature of periodic signal. In this study, we cast off the basic constraint of nonlinearity, extend it to a new type of generalized SR (GSR) in linear Langevin system, and propose the fluctuating-mass induced linear oscillator (FMLO). Then, by generalized scale transformation (GST), it is improved to be more suitable for exacting high-frequency fault features. Moreover, by analyzing the system stationary response, we find that the synergy of the linear system, internal random regulation and external excitement can conduct a rich variety of non-monotonic behaviors, such as bona-fide SR, conventional SR, GSR, and stochastic inhibition (SI). Based on the numerical implementation, it is found that these behaviors play an important role in adaptively optimizing system parameters to maximally improve the performance and identification ability of weak high-frequency signal in strong background noise. Finally, the experimental data are further performed to verify the effectiveness and superiority in comparison with traditional dynamical methods. The results show that the proposed GST-FMLO system performs the best in the bearing fault diagnoses of inner race, outer race and rolling element. Particularly, by amplifying the characteristic harmonics, the low harmonics become extremely weak compared to the characteristic. Additionally, the efficiency is increased by more than 5 times, which is significantly better than the nonlinear dynamical methods, and has the great potential for online fault diagnosis.
Highlights
IntroductionRolling bearing is one of the most critical aspects of modern machines, such as wind turbines, machine tools, centrifugal pumps, compressors, and motorized spindles [1]
Rolling bearing is one of the most critical aspects of modern machines, such as wind turbines, machine tools, centrifugal pumps, compressors, and motorized spindles [1].In complex operating environments, the faults are inevitable and even lead to the damage of whole equipment
We consider the adaptive performance of three different dynamical methods, i.e., GSTOBSR, generalized scale transformation (GST)-Duffing and GST-fluctuating-mass induced linear oscillator (FMLO), in the detection of weak signal with the actual driving frequency f in = 100 Hz, which is regarded as an unknown parameter
Summary
Rolling bearing is one of the most critical aspects of modern machines, such as wind turbines, machine tools, centrifugal pumps, compressors, and motorized spindles [1]. Compared with the traditional methods, i.e., overdamped bistable SR system and underdamped Duffing oscillator in the simulations, the proposed method is verified to be significantly better in both output SNR performance with the known driving frequency and adaptive identifying ability with the unknown driving frequency It shows the superiority in the experimental application of bearing fault diagnosis. GST method is combined to compensate for the shortcomings of low-frequency driving constraint It is verified from stationary response analysis that the proposed GST-FMLO system shows a rich variety of high-frequency induced GSR behaviors, the Sensors 2021, 21, 707 double-peak bona fide SR with GST coefficient, which can be adaptively utilized in the multi-parameter regulation by PSO algorithm with the strategy of decreasing inertial weight, and achieve the optimal energy conversion from mass fluctuation to weak highfrequency fault characteristic.
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