Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review
Qian Lv, Xiaoling Yu, Haihui Ma, Junchao Ye, Weifeng Wu, Xiaolin Wang
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.