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Applications of machine learning to reciprocating compressor fault diagnosis: a review

Citation

Lv, Q and Yu, X and Ma, H and Ye, J and Wu, W and Wang, X, Applications of machine learning to reciprocating compressor fault diagnosis: a review, Processes, 9, (6) Article 909. ISSN 2227-9717 (2021) [Refereed Article]


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Copyright 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).

DOI: doi:10.3390/pr9060909

Abstract

Operating condition detection and fault diagnosis are very important for reliable operation of reciprocating compressors. Machine learning is one of the most powerful tools in this field. However, there are very few comprehensive reviews which summarize the current research of machine learning in monitoring reciprocating compressor operating condition and fault diagnosis. In this paper, the recent application of machine learning techniques in reciprocating compressor fault diagnosis is reviewed. The advantages and challenges in the detection process, based on three main monitoring parameters in practical applications, are discussed. Future research direction and development are proposed.

Item Details

Item Type:Refereed Article
Keywords:reciprocating compressor, condition monitoring, fault diagnosis, machine learning
Research Division:Engineering
Research Group:Mechanical engineering
Research Field:Energy generation, conversion and storage (excl. chemical and electrical)
Objective Division:Energy
Objective Group:Energy storage, distribution and supply
Objective Field:Energy systems and analysis
UTAS Author:Wang, X (Professor Xiaolin Wang)
ID Code:144425
Year Published:2021
Web of Science® Times Cited:9
Deposited By:Engineering
Deposited On:2021-05-22
Last Modified:2021-10-14
Downloads:13 View Download Statistics

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