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Jianmin Zhao
Mechanical Engineering College, Shijiazhuang

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Bearing Fault Diagnosis Method Using Envelope Analysis and Euclidean Distance Haiping Li; Jianmin Zhao; Xinghui Zhang; Hongzhi Teng; Ruifeng Yang; Lishan Hao
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

Bearings are widely used in rotating machines. Its health status is a significant index to indicate whether machines run continually or not. Detecting the bearing faults timely is very important for the maintenance decision making. In this paper, a new fault diagnosis method based on envelope analysis and Euclidean Distance is developed. Envelope analysis is used to enable the fault frequencies clearly. Then, amplitudes of fault frequencies are used as the fault features. Finally, Euclidean Distance is used to identify the different fault types. This method can identify the fault locations intelligently even if the bearings are under different fault levels. The effectiveness of this methodology is demonstrated using the bearing data sets of Case Western Reserve Univerity. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4186
Gear Fault Diagnosis and Classification Based on Fisher Discriminant Analysis Haiping Li; Jianmin Zhao; Xinghui Zhang; Hongzhi Teng; Ruifeng Yang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 8: August 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i8.pp6198-6204

Abstract

Gears are the most essential parts in rotating machinery. So gear fault modes diagnosis and levels classification are very important in engineering practice. This paper present a novel method in gear fault recognition and identification using Fisher discriminant analysis (FDA) due to FDA can reduct dimension when analyse signal. The real data collected from a gearbox test rig is used to validate the method this paper proposed. And the effectiveness of the methodology was demonstrated by the results obtained from the analysis. Three kinds of fault modes and levels were identified. And energy was selected as feature parameter. The fault modes (normal, breaktooth and crack) were diagnosed at first, then the fault levels of breaktooth and crack are classified. The accurate rate of the method is approximate 89%.
Co-Authors Haiping Li Hongzhi Teng Lishan Hao Ruifeng Yang Xinghui Zhang