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

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Lv, Q, Yu, X, Ma, H, Ye, J, Wu, W and Wang, X ORCID: 0000-0003-4293-7523 2021 , 'Applications of machine learning to reciprocating compressor fault diagnosis: a review' , Processes, vol. 9, no. 6 , pp. 1-14 , doi: 10.3390/pr9060909.

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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 Type: Article
Authors/Creators:Lv, Q and Yu, X and Ma, H and Ye, J and Wu, W and Wang, X
Keywords: reciprocating compressor, condition monitoring, fault diagnosis, machine learning
Journal or Publication Title: Processes
Publisher: MDPIAG
ISSN: 2227-9717
DOI / ID Number: 10.3390/pr9060909
Copyright Information:

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

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