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Dr. Junchao Ye
Xi’an Jiaotong University

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Research Keywords & Expertise

0 Modal Testing
0 fault diagnosis
0 vibration analysis
0 FEA analysis
0 lumped parameter method

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Short Biography

male,fault diagnosis researcher

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Review
Published: 21 May 2021 in Processes
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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.

ACS Style

Qian Lv; Xiaoling Yu; Haihui Ma; Junchao Ye; Weifeng Wu; Xiaolin Wang. Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review. Processes 2021, 9, 909 .

AMA Style

Qian Lv, Xiaoling Yu, Haihui Ma, Junchao Ye, Weifeng Wu, Xiaolin Wang. Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review. Processes. 2021; 9 (6):909.

Chicago/Turabian Style

Qian Lv; Xiaoling Yu; Haihui Ma; Junchao Ye; Weifeng Wu; Xiaolin Wang. 2021. "Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review." Processes 9, no. 6: 909.