A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation
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Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan 430081, China
2
Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(17), 3755; https://doi.org/10.3390/s19173755
Received: 23 July 2019
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Revised: 23 August 2019
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Accepted: 27 August 2019
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Published: 30 August 2019
(This article belongs to the Special Issue Sensors for Fault Diagnosis)
Bearing fault diagnosis is of utmost importance in the maintenance of mechanical equipment. The collected fault vibration signal generally presents a modulated nature due to the special structure and dynamic characteristics of the bearings. This paper introduces a novel demodulation analysis technique via energy separation and local low-rank matrix approximation (LLORMA) to address this type of signal. The amplitude envelope and instantaneous frequency of the signal can be calculated via an energy separation algorithm based on the Teager energy operator. We can confirm the bearing faults by comparing the peak frequencies of the Fourier spectrum of the amplitude envelope and instantaneous frequency with the theoretical bearing fault-related frequencies. However, this algorithm is only suitable for handling single-component signals. In addition, the powerful background noise has a serious effect on the demodulation results. To tackle these problems, a new signal decomposition method based on LLORMA is proposed to decompose the signal into several single-components and eliminate the noise simultaneously. After that, the single-component signal representing the fault characteristics can be identified via the high frequency feature of the modulated signal. The analysis of the simulated signal and the bearing outer race fault signal collected from a bearing-gear fault test rig indicate that the proposed technique has an excellent diagnostic performance for bearing fault signals.
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Keywords:
bearing fault diagnosis; modulation signal; demodulation analysis; teager energy operator; local low-rank matrix approximation
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MDPI and ACS Style
Lv, Y.; Ge, M.; Zhang, Y.; Yi, C.; Ma, Y. A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation. Sensors 2019, 19, 3755. https://doi.org/10.3390/s19173755
AMA Style
Lv Y, Ge M, Zhang Y, Yi C, Ma Y. A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation. Sensors. 2019; 19(17):3755. https://doi.org/10.3390/s19173755
Chicago/Turabian StyleLv, Yong, Mao Ge, Yi Zhang, Cancan Yi, and Yubo Ma. 2019. "A Novel Demodulation Analysis Technique for Bearing Fault Diagnosis via Energy Separation and Local Low-Rank Matrix Approximation" Sensors 19, no. 17: 3755. https://doi.org/10.3390/s19173755
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