University of Illinois at Chicago
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A Data Mining based Full Ceramic Bearing Fault Diagnostic System using AE Sensors

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journal contribution
posted on 2012-03-06, 00:00 authored by D. He, R. Y. Li, J. D. Zhu, M. Zade
Full ceramic bearings are considered the first step towards full ceramic, oil free engines in the future. No research on full ceramic bearing fault diagnostics using acoustic emission (AE) sensors has been reported. Unlike their steel counterparts, signal processing methods to extract effective AE fault characteristic features and fault diagnostic systems for full ceramic bearings have not been developed. In this paper, a data mining based full ceramic bearing diagnostic system using AE based condition indicators (CIs) is presented. The system utilizes a new signal processing method based on Hilbert Huang transform (HHT) to extract AE fault features for the computation of CIs. These CIs are used to build a data mining based fault classifier using a k-nearest neighbor (KNN) algorithm. Seeded fault tests on full ceramic bearing outer race, inner race, balls, and cage are conducted on a bearing diagnostic test rig and AE burst data are collected. The effectiveness of the developed fault diagnostic system is validated using real full ceramic bearing seeded fault test data. *


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© 2011 by IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, Ieee Transactions on Neural Networks; Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. DOI: 10.1109/TNN.2011.2169087


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