Applications of Machine Learning to Reciprocating Compressor Fault Diagnosis: A Review

被引:24
作者
Lv, Qian [1 ]
Yu, Xiaoling [1 ]
Ma, Haihui [1 ]
Ye, Junchao [1 ]
Wu, Weifeng [1 ]
Wang, Xiaolin [2 ]
机构
[1] Xi An Jiao Tong Univ, Sch Energy & Power Engn, Xian 710049, Peoples R China
[2] Univ Tasmania, Sch Engn, Hobart, Tas 7001, Australia
基金
中国国家自然科学基金;
关键词
reciprocating compressor; condition monitoring; fault diagnosis; machine learning; ACOUSTIC-EMISSION PARAMETERS; CONDITION CLASSIFICATION; INTELLIGENCE; RECOGNITION; NETWORKS; SYSTEM; VALVES;
D O I
10.3390/pr9060909
中图分类号
TQ [化学工业];
学科分类号
0817 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 73 条
[1]   Feature Selection and Fault Classification of Reciprocating Compressors using a Genetic Algorithm and a Probabilistic Neural Network [J].
Ahmed, M. ;
Gu, F. ;
Ball, A. .
9TH INTERNATIONAL CONFERENCE ON DAMAGE ASSESSMENT OF STRUCTURES (DAMAS 2011), 2011, 305
[2]  
Ahmed M., 2011, P 17 INT C AUT COMP
[3]  
Ahmed M., 2014, P 20 INT C AUT COMP
[4]   Automated Valve Fault Detection Based on Acoustic Emission Parameters and Artificial Neural Network [J].
Al-Obaidi, Salah M. Ali ;
Hui, K. H. ;
Hee, L. M. ;
Leong, M. Salman ;
Abdul-Hussain, Mahdi Ali ;
Abdelrhman, Ahmed M. ;
Ali, Y. H. .
ENGINEERING APPLICATION OF ARTIFICIAL INTELLIGENCE CONFERENCE 2018 (EAAIC 2018), 2019, 255
[5]   A Comparative Experimental Study on the Use of Machine Learning Approaches for Automated Valve Monitoring Based on Acoustic Emission Parameters [J].
Ali, Salah M. ;
Hui, K. H. ;
Hee, L. M. ;
Leong, M. Salman ;
Al-Obaidi, M. A. ;
Ali, Y. H. ;
Abdelrhman, Ahmed M. .
3RD INTERNATIONAL CONFERENCE ON MECHANICAL, MANUFACTURING AND PROCESS PLANT ENGINEERING (ICMMPE 2017), 2018, 328
[6]   Automated valve fault detection based on acoustic emission parameters and support vector machine [J].
Ali, Salah M. ;
Hui, K. H. ;
Hee, L. M. ;
Leong, M. Salman .
ALEXANDRIA ENGINEERING JOURNAL, 2018, 57 (01) :491-498
[7]  
Ali YH, 2014, J TEKNOL, V69
[8]  
[Anonymous], 2002, PRESS SERIES
[9]  
[Anonymous], 2006, E1316 ASTM ASTM INT
[10]  
Bloch H.P., 2006, PRACTICAL GUIDE COMP