Weak fault feature extraction method based on compound tri-stable stochastic resonance |
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| Affiliation: | 1. Department of Mechanical Engineering, University of Science and Technology Beijing 100083, PR China;2. Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, M5S 3G8, Canada;1. School of Electronics and Information Engineering, Hengshui University, Hebei 053000, PR China;2. Department of Physics, College of Science, Nanjing Agricultural University, Nanjing 210095, PR China;1. Department of Mathematics, Faculty of Arts and Sciences, Near East University, Nicosia 99138, Cyprus;2. Department of Physics, Chemistry and Mathematics, Alabama A&M University, Normal, AL 35762–4900, USA;3. Department of Mathematics, King Abdulaziz University, Jeddah 21589, Saudi Arabia;4. Department of Applied Mathematics, National Research Nuclear University 31 Kashirskoe Shosse, Moscow 115409, Russian Federation;5. Department of Mathematics and Statistics, Tshwane University of Technology, Pretoria 0008, South Africa;6. Department of Mathematics, Faculty of Science and Arts, Yozgat Bozok University, Yozgat 66100, Turkey;7. Departamento de Matematicas Aplicadas y Sistemas, Universidad Autnoma Metropolitana–Cuajimalpa, Vasco de Quiroga 4871, Mexico City 05348, Mexico;8. Radiation Physics Laboratory, Department of Physics, Faculty of Sciences, Badji Mokhtar University, P. O. Box 12, Annaba 23000, Algeria;9. Faculty of Sciences and Environment, Department of Chemistry, Physics and Environment, Dunarea de Jos University of Galati, 47 Domneasca Street, 800008, Romania;10. Science Program, Texas A&M University at Qatar, Doha, PO Box 23874, Qatar;1. School of Electrical Engineering, Yanshan University, Qinhuangdao, 066004, PR China;2. School of Vehicles and Energy, Yanshan University, Qinhuangdao, 066004, PR China |
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| Abstract: | In the presence of strong background noise, in view of the difficulty in extracting weak fault features, a new compound tri-stable stochastic resonance (CTSR) model is proposed by combining the Gaussian Potential model and the mixed bi-stable model. Compared with the traditional tri-stable stochastic resonance (TTSR) method, all parameters of CTSR model have no coupling characteristics. Therefore, the output signal-to-noise ratio (SNR) can be easily optimized by adjusting the system parameters. The CTSR model retains the advantages of constraint and continuity of the Gaussian Potential model, and has a higher utilization rate of noise. Finally, through bearing and engineering experiments, the outstanding advantages of the proposed method in feature extraction of weak faults are verified. |
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