Maximum cross-correlated kurtosis-based unsaturated stochastic resonance and its application to bearing fault diagnosis |
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| Institution: | 1. School of Mechanical Engineering, Tiangong University, Tianjin, China;2. School of Electronics and Information Engineering, Tiangong University, Tianjin, China;3. School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, Zhejiang, China;4. The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China;5. School of Mathematical Sciences, Tiangong University, Tianjin, China;6. School of Physical Science and Technology, Tiangong University, Tianjin, China;7. School of Economics and Management, Tianjin Chengjian University, Tianjin, China;1. School of Vehicles and Energy, Yanshan University, Qinhuangda, 066004, China;2. School of Electrical Engineering, Yanshan University, Qinhuangda, 066004, China;1. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing 210023, China;2. School of Mechanical Engineering and Mechanics, Ningbo University, Ningbo 315211, Zhejiang, China;3. College of Modern Posts, Nanjing University of Posts and Telecommunications, Nanjing 210003, China;4. Department of Electrical Engineering and Information Systems, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113–8656, Japan |
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| Abstract: | Considering the random impulses of mechanical noise and the limitations involved while identifying mechanical fault impulse signals via traditional measurement indices of signal-to-noise ratio, which require the characteristic frequency to be known in advance, this study proposes an adaptive unsaturated stochastic resonance method employing maximum cross-correlated kurtosis as the signal detection index. The proposed method combines the features of a cross-correlated coefficient to indicate periodic fault transients and those of spectrum kurtosis to locate these transients in the frequency domain. Actual vibration signals collected from motor and gear bearings subjected to heavy noise are used to demonstrate the effectiveness of the proposed method. Through a coarse tree-based machine learning method, the proposed method is verified to be more suitable for explaining the periodic impulse components of bearing signals, as compared to the ensemble empirical mode decomposition denoising method and unsaturated stochastic resonance using the kurtosis-intercorrelation index. |
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