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Title: A Novel Signal Processing Approach for Valve Health Condition Classification of aReciprocating Compressor with Seeded Faults Considering Time-Frequency Partitions
Authors: Yih-Hwang Lin;Wen-Sheng Lee;Chung-Yung Wu
Contributors: NTOU:Department of Mechanical and Mechatronic Engineering
Keywords: automated condition classification, reciprocating compressor, time-frequency analysis
Date: 2012-12
Issue Date: 2013-10-07T02:07:54Z
Publisher: Journal of Marine Science and Technology
Abstract: This study deals with a novel signal processing approach for automated valve condition classification of a reciprocating compressor with seeded faults. The classification system consists of a front end time-frequency analysis platform for the vibration signal measured, fault feature vectors for making the formidable amount of time-frequency data manageable, and a probabilistic neural network for automatic classification without the intervention of human experts. Rather than representing each time-frequency data set with one single feature vector comprising three indices, namely time, frequency, and amplitude,the time-frequency plane is further partitioned into an appropriate number of sub-regions to enhance the characteristics representation of the time-frequency data. This study shows that a flawless classification can be realized by using the proposed approach with appropriate selections of index modification method and number of time-frequency sub-regions without resorting to the removal of similar fault cases.
Appears in Collections:[Department of Mechanical and Mechatronic Engineering] Periodical Articles

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