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|Title: ||A novel signal processing approach for valve health condition classification of a reciprocating compressor with seeded faults considering time-frequency partitions|
|Authors: ||Y.-H. Lin;W.-S. Lee;C.-Y. Wu|
|Issue Date: ||2017-04-20T01:02:41Z
|Publisher: ||Journal of Marine Science and Technology|
|Abstract: ||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 subregions without resorting to the removal of similar fault cases .|
|Relation: ||21(5), pp.578-585|
|Appears in Collections:||[機械與機電工程學系] 期刊論文|
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