Sim, Hoi Yin and Ramli, Rahizar and Saifizul, Ahmad Abdullah (2020) Assessment of characteristics of acoustic emission parameters for valve damage detection under varying compressor speeds. Proceedings of the Institution of Mechanical Engineers Part C-Journal of Mechanical Engineering Science, 234 (17). pp. 3521-3540. ISSN 0954-4062, DOI https://doi.org/10.1177/0954406220915232.
Full text not available from this repository.Abstract
Acoustic emission technique is often employed to detect valve abnormalities. With the development of technology, machine learning-based fault diagnosis methods are prevalent in the nondestructive testing industry as they can automatically detect valve problems without any human intervention. Nevertheless, feeding in all possible input parameters into the learning algorithm without any prior assessment may result in high computational cost and time, while adding to the risk of having false alarms. This study intended to obtain characteristics of acoustic emission signal for various valve conditions and compressor speeds by examining the four most commonly used parameters, namely the acoustic emission root mean square, acoustic emission crest factor, acoustic emission variance, and acoustic emission kurtosis. The study begins with time-frequency analysis of one revolution acoustic emission signal acquired from a faulty suction valve through discrete wavelet transform to obtain the signal characteristics of valve events. To associate signals with valve movements, the reconstructed discrete wavelet transform signals are further segregated into six time segments, and the four acoustic emission parameters are computed from each of the time segments. These parameters are analyzed through statistical analysis namely the two-way analysis of variance, followed by the Tukey test to obtain the best parameter which can differentiate each valve condition clearly at all speeds. The results revealed that acoustic emission root mean square is the best parameter especially in identification of heavy grease valve condition during suction valve opening event while acoustic emission crest factor is capable to detect leaky valve during the suction valve closing event at all speeds. It is believed that effective valve diagnosis strategy can be delivered by referring to the features of parameters and the characteristic valve event timing corresponding to each valve condition and speed.
| Item Type: | Article |
|---|---|
| Funders: | UNSPECIFIED |
| Uncontrolled Keywords: | Acoustic emission; Fault diagnosis; Valves; Reciprocating compressor; Wavelet transform; Statistical analysis; Machine condition monitoring |
| Subjects: | T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
| Divisions: | Faculty of Engineering Faculty of Engineering > Department of Mechanical Engineering |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 02 May 2024 04:18 |
| Last Modified: | 02 May 2024 04:18 |
| URI: | http://eprints.um.edu.my/id/eprint/36760 |
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