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Materials 2017, 10(6), 675; doi:10.3390/ma10060675

Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions

Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
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Received: 15 April 2017 / Revised: 14 June 2017 / Accepted: 15 June 2017 / Published: 20 June 2017
(This article belongs to the Special Issue Structural Health Monitoring for Aerospace Applications 2017)
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Abstract

Varying speed conditions bring a huge challenge to incipient fault detection of rolling element bearings because both the change of speed and faults could lead to the amplitude fluctuation of vibration signals. Effective detection methods need to be developed to eliminate the influence of speed variation. This paper proposes an incipient fault detection method for bearings under varying speed conditions. Firstly, relative residual (RR) features are extracted, which are insensitive to the varying speed conditions and are able to reflect the degradation trend of bearings. Then, a health indicator named selected negative log-likelihood probability (SNLLP) is constructed to fuse a feature set including RR features and non-dimensional features. Finally, based on the constructed SNLLP health indicator, a novel alarm trigger mechanism is designed to detect the incipient fault. The proposed method is demonstrated using vibration signals from bearing tests and industrial wind turbines. The results verify the effectiveness of the proposed method for incipient fault detection of rolling element bearings under varying speed conditions. View Full-Text
Keywords: incipient fault detection; varying speed; alarm trigger mechanism; adaptive threshold; rolling element bearings incipient fault detection; varying speed; alarm trigger mechanism; adaptive threshold; rolling element bearings
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Xue, L.; Li, N.; Lei, Y.; Li, N. Incipient Fault Detection for Rolling Element Bearings under Varying Speed Conditions. Materials 2017, 10, 675.

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