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The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum

Rehab, Ibrahim, Tian, Xiange, Gu, Fengshou and Ball, Andrew (2014) The fault detection and severity diagnosis of rolling element bearings using modulation signal bispectrum. In: Eleventh International Conference on Condition Monitoring and Machinery Failure Prevention Technologies, 10th - 12th June 2014, Manchester, UK.

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Abstract

The rolling element bearing is a key part in many mechanical equipment. The accurate and timely diagnosis of its faults is critcal for predictive maintenance. Vibration signals from a defective bearing with a localized fault contain a series of impulsive responses, which result from the impacts of the defective part(s) with other elements and inevitable noise. Most researches carried out have focused on fault location identification. However, limited work has been reported for fault severity estimation, which is critical to make decision for maintenance actions. To improve current diagnostic capability,. This paper presents a new approach to detection and diagnosis of bearing fault severity based on vibration analysis using modulation signal bispectrum (MSB). It models the vibration sources from bearing defects as an impact process with constant size but three different lengths corresponding to outer race fault, inner race fault and roller fault, respectively. The results shows that MSB has a better and reliable performance in extract small changes from the faulty bearing for accurate fault detection and diagnosis for different bearing fault severity.

Item Type: Conference or Workshop Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Schools: School of Computing and Engineering
School of Computing and Engineering > Automotive Engineering Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Energy, Emissions and the Environment Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Machinery Condition and Performance Monitoring Research Group
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > High-Performance Intelligent Computing
School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
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Depositing User: Cherry Edmunds
Date Deposited: 30 Jun 2014 15:00
Last Modified: 30 Nov 2016 21:12
URI: http://eprints.hud.ac.uk/id/eprint/21053

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