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Fault Detection of Reciprocating Compressor for Small-Type Refrigerators Using ART-Kohonen Networks and Wavelet Analysis  

Yang, Bo-Suk (School of Mechanical Engineering, Pukyong National University)
Lee, Soo-Jong (School of Mechanical Engineering, Pukyong National University)
Han, Tian (School of Mechanical Engineering, University of Science and Technology Beijing)
Publication Information
Journal of Mechanical Science and Technology / v.20, no.12, 2006 , pp. 2013-2024 More about this Journal
Abstract
This paper proposes a condition classification system using wavelet transform, feature evaluation and artificial neural networks to detect faulty products on the production line of reciprocating compressors for refrigerators. The stationary features of vibration signals are extracted from statistical cumulants of the discrete wavelet coefficients and root mean square values of band-pass frequencies. The neural networks are trained by the sample data, including healthy or faulty compressors. Based on training, the proposed system can be used on the automatic mass production line to classify product quality instead of people inspection. The validity of this system is demonstrated by the on-site test at LG Electronics, Inc. for reciprocating compressors. According to different products, this system after some modification may be useful to increase productivity in different types of production lines.
Keywords
Fault Classification; Feature Extraction; Wavelet Transform; Neural Network; Reciprocating Compressor;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
Times Cited By Web Of Science : 1  (Related Records In Web of Science)
Times Cited By SCOPUS : 1
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