Abstract
We investigate a multi-frequency signal that cannot be decomposed by empirical mode decomposition directly. Moreover, this kind of signal in the noisy background cannot be decomposed successfully by the traditional stochastic resonance with bistable system yet. We propose a new method which using the empirical mode decomposition combined the adaptive stochastic resonance in a new periodical model to solve this problem. The results show that the proposed method decomposes the multi-frequency signal perfectly. Meanwhile, the general scale transformation and random particle swarm optimization algorithm are used to help obtain a better result in the process of optimization. Through using this new method, the simulation results are satisfactory. More importantly, this new method also shows good performance in the application of bearing fault diagnosis.
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Jingling Zhang received her B.Sc. degree in 2016 from Jiangsu Normal University, Jiangsu, China. Now she is a master degree candidate in China University of Ming and Technology. Her main research interest is equipment fault diagnosis.
Dawen Huang received his B.Sc. degree in 2016 from Heibei University of Engineering, Heibei, China. Now he is a master degree candidate in China University of Ming and Technology. His main research interest is equipment fault diagnosis.
Jianhua Yang received the Ph.D. degree in State Key Laboratory of Mechanics and Control of Mechanical Structures from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2011. Now he is an Associate Professor in China University of Mining and Technology. His main research interest includes equipment fault diagnosis, intelligent maintenance system, nonlinear vibration, and novel engineering machineries.
Xiaole Liu received his B.Sc. degree and is a master degree candidate in China University of Ming and Technology. His main research interest is equipment fault diagnosis.
Houguang Liu received the Ph.D. degree in State Key Laboratory of Mechanical System and Vibration from Shanghai Jiao Tong University, Shanghai, China, in 2011. Now he is an Associate Professor in China University of Mining and Technology. His current research interests include mechanical vibration analysis and control, vibration signal processing, and fault diagnosis.
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Zhang, J., Huang, D., Yang, J. et al. Realizing the empirical mode decomposition by the adaptive stochastic resonance in a new periodical model and its application in bearing fault diagnosis. J Mech Sci Technol 31, 4599–4610 (2017). https://doi.org/10.1007/s12206-017-0906-6
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DOI: https://doi.org/10.1007/s12206-017-0906-6