Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics |
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| Institution: | 1. Center for Intelligent Maintenance Systems, University of Cincinnati, OH 45221, USA;2. Institute of Acoustics, Chinese Academy of Sciences, 17 Zhongguancun Street, Haidian, Beijing 100080, China;3. Department of Mechanical and Industrial Engineering, Northeastern University, 60 Huntington Ave 334SN, Boston, MA 02115, USA;1. Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100124, China;2. School of Mechanical Engineering, Xinjiang University, Urumq 830047, China;1. NSF I/UCR Center for Intelligent Maintenance System, University of Cincinnati, USA;2. State Key Laboratory for Manufacturing Systems Engineering, Research Institute of Diagnostics and Cybernetics, Xi''an Jiaotong University, Xi''an 710049, China;1. School of Mechanical Engineering, Xi′an Jiaotong University, Xi′an 710049, China;2. Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi''an Jiaotong University, Xi''an 710049, China |
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| Abstract: | De-noising and extraction of the weak signature are crucial to fault prognostics in which case features are often very weak and masked by noise. The wavelet transform has been widely used in signal de-noising due to its extraordinary time-frequency representation capability. In this paper, the performance of wavelet decomposition-based de-noising and wavelet filter-based de-noising methods are compared based on signals from mechanical defects. The comparison result reveals that wavelet filter is more suitable and reliable to detect a weak signature of mechanical impulse-like defect signals, whereas the wavelet decomposition de-noising method can achieve satisfactory results on smooth signal detection. In order to select optimal parameters for the wavelet filter, a two-step optimization process is proposed. Minimal Shannon entropy is used to optimize the Morlet wavelet shape factor. A periodicity detection method based on singular value decomposition (SVD) is used to choose the appropriate scale for the wavelet transform. The signal de-noising results from both simulated signals and experimental data are presented and both support the proposed method. |
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