Sim, Hoi Yin and Ramli, Rahizar and Saifizul, Ahmad Abdullah and Soong, Ming Foong (2020) Detection and estimation of valve leakage losses in reciprocating compressor using acoustic emission technique. Measurement, 152. p. 107315. ISSN 0263-2241, DOI https://doi.org/10.1016/j.measurement.2019.107315.
Full text not available from this repository.Abstract
Valve problems in reciprocating compressor are often resolved through parameter analysis of acoustic emission (AE) signals or intelligent system without examining the nature of signals related to its source. This study intended to explore the potential of AE signal for the measurement of valve flow rate in order to quantify the severity of valve problems. The study started with time-frequency analysis of AE signal through discrete wavelet transform, followed by valve condition classification and valve flow rate estimation for faulty valves operated from 450 to 750 rpm. The k-nearest neighbours (KNN) and support vector machine (SVM) classification algorithms are employed to classify the valve conditions before estimation of valve flow rate through regression model. The prediction accuracy of valve flow models is found between 74.5 and 98.8%. Finally, the valve leakage loss can be estimated by computing the difference of flow rate between the measured valve and its baseline (normal valve) using AE parameter. © 2019 Elsevier Ltd
| Item Type: | Article |
|---|---|
| Funders: | Ministry of Science, Technology, and Innovation of Malaysia (Project no.: 03-01-03-SF1033 / SF005-2015), Institute of Research Management and Monitoring (IPPP) from University of Malaya, Malaysia (Project no.: PG233-2014B) |
| Uncontrolled Keywords: | Acoustic emission; Fault diagnosis; Reciprocating compressor; Valve leakage loss; K-nearest neighbours (KNN); Support vector machine (SVM) |
| Subjects: | Q Science > Q Science (General) Q Science > QC Physics |
| Divisions: | Faculty of Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 23 Jun 2020 04:34 |
| Last Modified: | 23 Jun 2020 04:34 |
| URI: | http://eprints.um.edu.my/id/eprint/24938 |
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