Dongying Han1 , Shujun An2 , Peiming Shi3 , Ying Zhang4
1School of Vehicles and Energy, Yanshan University, Qinhuangdao, Hebei, 066004, P. R. China
1, 3, 4School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, 30332, USA
2, 3School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei, 066004, P. R. China
Journal of Vibroengineering, Vol. 20, Issue 1, 2018, p. 258-271.
Received 23 June 2017; received in revised form 3 November 2017; accepted 26 November 2017; published 15 February 2018
Copyright © 2018 JVE International Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
For detecting the weak fault diagnosis submerged in heavy noise, a new method called multi-scale cascaded multi-stable stochastic resonance (MCMSR) is studied. The method can effectively extract weak fault diagnosis from noise background using multi-scale wavelet noise tuning stochastic resonance (SR). Firstly, input signal with noise is decomposed by multi-scale wavelets transformation, and each scale signal is adjusted by scaling factor, then the decomposed signal is used as the input of cascaded multi-stable systems to achieve the detection of fault diagnosis. If the input signal is a large parameter signal, to conform to the conditions of SR, the decomposed signal must be processed by twice sampling. The simulation and experimental signals are carried out to test the feasibility of the method. From the signal to noise ratio (SNR) comparison curves of original signal, SR output signal and MCMSR output signal plotted together, we can find that the useful signal can be enhanced by MCMSR method than SR method. The experimental results indicate that the MCMSR can extract fault diagnosis from heavy background noise.
Keywords: weak fault diagnosis, wavelet transforms, multi-scale, multi-stable stochastic resonance.
This work is supported by the National Natural Science Foundation of China (Grant No. 51475407), Key Project of Natural Science Research in Colleges and Universities of Hebei Province (Grant No. ZD2015050) and Hebei Provincial Natural Science Foundation of China (No. E2015203190).