processes Review Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review Qian Lv 1, Xiaoling Yu 1,*, Haihui Ma 1, Junchao Ye 1, Weifeng Wu 1 and Xiaolin Wang 2 1 School of Energy and Power Engineering, Xi’an Jiaotong University, Xi’an 710049, China;
[email protected] (Q.L.);
[email protected] (H.M.);
[email protected] (J.Y.);
[email protected] (W.W.) 2 School of Engineering, University of Tasmania, Hobart, TAS 7001, Australia;
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[email protected] 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. Keywords: reciprocating compressor; condition monitoring; fault diagnosis; machine learning Citation: Lv, Q.; Yu, X.; Ma, H.; Ye, J.; 1. Introduction Wu, W.; Wang, X. Applications of The reciprocating compressor (RC) is a key piece of equipment in petroleum and Machine Learning to Reciprocating chemical industries. If the RC does not operate in the rated efficiency, it will lead to great Compressor Fault Diagnosis: A economic loss to the company.