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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/41937

Title: Automated Fault Classification of Reciprocating Compressors from Vibration Data: A Case Study on Optimization using Genetic Algorithm
Authors: Yih-Hwang Lin;Wen-Sheng Lee;Chung-Yung Wu
Contributors: 國立臺灣海'洋大學:機械與機電工程學系
Keywords: Fault Classification;Reciprocating Compressor;Genetic Algorithm
Date: 2014
Issue Date: 2017-04-14
Publisher: Procedia Engineering
Abstract: Abstract:This article deals with automated fault classification of reciprocating compressors from vibration data. The genetic algorithm was applied to automate the process. A total of 15 fault cases based on practical observation of the machine faults was considered. Vibration data for the various fault cases were collected and processed using the time-frequency analysis, namely the short time Fourier transform (STFT), the smoothed pseudo Wigner-Ville distribution (SPWVD), and the reassigned smoothed pseudo Wigner-Ville distribution (RSPWVD), due to the non-stationary vibration characteristics of the system analyzed. The fault features for the formidable amount of time-frequency data were extracted first and fed into an artificial neural network for fault classification. It is demonstrated in this work that it is feasible to apply the genetic algorithm to automate the fault classification process and thereby minimize the requirement for intervention from the human experts.
Relation: 79, pp.355-361
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/41937
Appears in Collections:[機械與機電工程學系] 期刊論文

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