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Title of Journal: J Mech Sci Technol

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Abbravation: Journal of Mechanical Science and Technology

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Korean Society of Mechanical Engineers

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DOI

10.1016/0016-0032(82)90371-4

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ISSN

1976-3824

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Gearbox health condition identification by neurof

Authors: Long Zhang Guoliang Xiong Leping Liu Qingsong Cao
Publish Date: 2013/03/22
Volume: 27, Issue: 3, Pages: 603-608
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Abstract

A neurofuzzy ensemble NFE model has been investigated for machinery health diagnosis The proposed diagnosis system was illustrated by discriminating between various gear health conditions of a motorcycle gearbox Four different health scenarios were considered in this work normal slightworn mediumworn and brokenteeth gear Experimental results show the NFE model performs better than single neurofuzzy NF model with respect to classification accuracy sensitivity and specificity while the computational complexity is not increased significantly In addition the NFbased models are able to interpret their reasoning behavior in an intuitively understandable way as fuzzy ifthen rules which allows users to gain a deep insight into the dataLong Zhang is currently a lecturer at Mechatronics Engineering School of East China Jiao Tong University China He received his PhD degree from Shanghai Jiaotong University China in Jan 2011 His current research interests focus on vibration analysis signal processing artificial intelligence and their applications to machinery condition monitoring and fault diagnosis

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