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- W3130483442 abstract "Abstract Presented in this paper is the development of a vibration-based novelty detection algorithm for locating and identifying valve wear within industrial reciprocating compressors through the combined use of time-frequency analysis, image-based pattern recognition, and one-class support vector machines. A commonly reported cause of valve wear-related machine downtime is wear in the valve seat, causing a change in flow profile into and out of the compression chamber. Seeded faults are introduced into the valve manifolds of the ESH-1 industrial compressor and vibration data collected and separated into individual crank cycles before being analyzed using time-frequency analysis. The result is processed as an image and features used for classification are extracted using 1st and 2nd order images statistics and shape factors. A one-class support vector machine learning algorithm is then trained using data collected during healthy operation and then used to both detect and locate anomalous valve behavior with a greater than 82% success rate." @default.
- W3130483442 created "2021-03-01" @default.
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- W3130483442 date "2020-11-16" @default.
- W3130483442 modified "2023-09-26" @default.
- W3130483442 title "A Novelty Detection Approach to Compressor Valve Fault Detection and Localization" @default.
- W3130483442 doi "https://doi.org/10.1115/imece2020-23314" @default.
- W3130483442 hasPublicationYear "2020" @default.
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