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Weak signal detection method based on underdamped mixed potential stochastic resonance
Published:2022-12-20 author:REN Xue-ping, LI Fei, LI Zhi-xing. Browse: 1113 Check PDF documents
Weak signal detection method based on underdamped 
mixed potential stochastic resonance


REN Xue-ping1, LI Fei1, LI Zhi-xing1,2


(1.School of Mechanical Engineering, Inner Mongolia University of Science and Technology, Baotou 

014010, China; 2.Beijing Key Laboratory of Performance Guarantee on Urban Rail Transit Vehicles, 

Beijing University of Civil Engineering and Architecture, Beijing 102612, China)


Abstract: Aiming at the diagnosis of faint faults in mechanical equipment under strong noise background, a weak signal detection method of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)-wavelet threshold noise reduction method combined with underdamped mixed potential stochastic resonance (UMPSR) was proposed. Firstly, an underdamped mixed potential stochastic resonance model was established, the characteristics of the potential function were described, the output signal-to-noise ratio of the system was derived theoretically and the relationship between the signal-to-noise ratio and the noise intensity under different parameters was analyzed. Then, the original signal was preprocessed, the reconstructed signal from the noise reduction method was input into the system model and the system parameters were optimized by using the adaptive simulated annealing particle swarm algorithm to achieve the best matching of the stochastic resonant system. Finally, the proposed method was applied to the experiment of simulating the fault signal and the actual rolling bearing inner ring fault signal. The results were compared with those obtained by the mixed potential stochastic resonance (MPSR) method. The research results show that compared with the mixedpotential stochastic resonance method, the proposed underdamped mixed potential stochastic resonance method has higher spectral peaks and less disturbed by noise at fault frequencies of 50 Hz and 212.85 Hz, which can effectively improve the rolling bearing fault signal detection capability.

Key words: mechanical fault diagnosis; complete ensemble empirical mode decomposition with adaptive noise(CEEMDAN); wavelet threshold noise reduction; underdamped mixed potential stochastic resonance(UMPSR); stochastic resonance; noise intensity; signaltonoise ratio; spectral peak

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