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Bearing Health monitoring based on Hilbert-Huang Transform, Support Vector Machine and Regression.

Abstract : The detection, diagnostic and prognostic of bearing degradation play a key role in increasing the reliability and safety of electrical machines especially in key industrial sectors. This paper presents a new approach which combines the Hilbert-Huang transform, the support vector machine and the support vector regression for the monitoring of ball bearings. The proposed approach uses the Hilbert-Huang transform to extract new heath indicators from stationary/non-stationary vibration signals able to tack the degradation of the critical components of bearings. The degradation states are detected by a supervised classification technique called support vector machine and the fault diagnostic is given by analyzing the extracted health indicators. The estimation of the remaining useful life is obtained by a one-step time series prediction based on support vector regression. A set of experimental data collected from degraded bearings is used to validate the proposed approach. Experimental results show that the use of the Hilbert-Huang transform, the support vector machine and the support vector regression is a suitable strategy to improve the detection, diagnostic and prognostic of bearing degradation.
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https://hal.archives-ouvertes.fr/hal-01026491
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Submitted on : Monday, July 21, 2014 - 4:47:00 PM
Last modification on : Thursday, January 13, 2022 - 12:00:19 PM
Long-term archiving on: : Monday, November 24, 2014 - 9:26:41 PM

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Abdenour Soualhi, Kamal Medjaher, Noureddine Zerhouni. Bearing Health monitoring based on Hilbert-Huang Transform, Support Vector Machine and Regression.. IEEE Transactions on Instrumentation and Measurement, 2014, pp.1-11. ⟨10.1109/TIM.2014.2330494⟩. ⟨hal-01026491⟩

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