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| Rolling Bearing Fault Diagnosis Method Based on Singular Value Decomposition and Independent Component Analysis |
| CHEN Jian1,2,LIU Yuan-yuan1,2,HUANG Kai-xuan1,2,YANG Bin1,2,LIU Xing-fu1,2,CAI Kun-qi1,2 |
1. Institute of Sound and Vibration Research, Hefei University of Technology, Hefei, Anhui 230009, China
2. Automotive NVH Engineering & Technology Research Center of Anhui Province, Hefei, Anhui 230009, China |
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Abstract To solve the problem that it is difficult to extract the characteristic frequency of the fault in the early fault signal of the rolling bearing under strong background noise, the signal analysis method of singular value decomposition-independent component analysis was proposed. At first, phase space reconstruction was used to extend the one-dimensional time-domain signal to higher dimensions, and obtain the attractor trajectory matrix. Then singular value decomposition was performed on the trajectory matrix to reduce noise. According to the singular value difference spectrum threshold principle, the corresponding order components were selected for recombination to construct the virtual noise channel. Then the recombined signal and the observation signal were separated by ICA. Finally the energy operator demodulation method was used to extract the effective fault feature components to identify the fault type. The fault diagnosis experiment and simulation results of rolling bearing showed that the method is effective and feasible.
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Received: 26 January 2021
Published: 30 June 2022
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2022, Vol. 43 
