Diagnosis of air compressor condition using minimum redundancy maximum relevance (MRMR) algorithm and distance metric based classification
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Mechanical Engineering/College of Engineering/ Wasit University/.
Mechanical Engineering/ Wasit University/College of Engineering.
Civil Engineering/College of Engineering/ Wasit University/.
Submission date: 2021-08-17
Final revision date: 2021-10-28
Acceptance date: 2021-11-06
Online publication date: 2021-11-08
Publication date: 2021-11-08
Corresponding author
Hussein Razzaq Al-Bugharbee   

Mechanical Engineering/College of Engineering/ Wasit University/.
Diagnostyka 2021;22(4):25-32
Abstract: Finding a reliable machines condition monitoring technique has been attracted many researchers to avoid the sudden failure in machines and the unexpected consequences. This work proposes a combined fault diagnosis methodology of air compressors using frequency-based features and distance metric-based classification. The analyzed experimental datasets contain one healthy condition and seven different fault conditions. Features are extracted from the frequency spectrum, then the best feature sets are selected using MRMR algorithm. Eventually the classification is conducted using a distance metric classifier. The results demonstrated the automatic classification can reach 99% correct classification rate. The effect of selected feature set size, training sample size on the classification accuracy is also investigated. From the results, this method of analysis can be used efficiently for early detection of faults with very great accuracy.
Elhaj M, Gu F, Ball A, Albarbar A, Al-Qattan M, Naid A. Numerical simulation and experimental study of a two-stage reciprocating compressor for condition monitoring. Mechanical Systems and Signal Processing. 2008;22(2):374-389. https://doi.org/10.1016/j.ymss....
Pichler K, Lughofer E, Pichler M, Buchegger T, Klement EP, Huschenbett M. Fault detection in reciprocating compressor valves under varying load conditions. Mechanical Systems and Signal Processing. 2016; 70:104-119. https://doi.org/10.1016/j.ymss....
Wang Y, Xue C, Jia X, Peng X. Fault diagnosis of reciprocating compressor valve with the method integrating acoustic emission signal and simulated valve motion. Mechanical systems and signal Processing. 2015;56:197-212. https://doi.org/10.1016/j.ymss....
Verma NK, Gupta R, Sevakula RK, Salour A. Signal transforms for feature extraction from vibration signal for air compressor monitoring. TENCON 2014-2014 IEEE Region 10 Conference, 2014: 1-6.
Verma NK, Kadambari J, Abhijit B, Tanu S, alour A. Finding sensitive sensor positions under faulty condition of reciprocating air compressors. 2011 IEEE Recent Advances in Intelligent Computational Systems, 2011:242-246.
Tran VT, AlThobiani F, Tinga T, Ball A, Niu G. Single and combined fault diagnosis of reciprocating compressor valves using a hybrid deep belief network. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2018;232(20):3767-3780. https://doi.org/10.1177/095440....
Tabrizi AA, Al-Bugharbee H, Trendafilova I, Garibaldi L. A cointegration-based monitoring method for rolling bearings working in time-varying operational conditions. Meccanica 2017; 52(4-5): 1201-1217. https://doi.org/10.1007/s11012....
Liu Y, Duan L, Yuan Z, Wang N, Zhao J. An intelligent fault diagnosis method for reciprocating compressors based on LMD and SDAE. Sensors 2019; 19(5): 1041. https://doi.org/10.3390/s19051....
Li CJ, Yu X. High pressure air compressor valve fault diagnosis using feedforward neural networks. Mechanical Systems and Signal Processing. 1995; 9(5):527-536. https://doi.org/10.1006/mssp.1....
Verma NK, Roy A, Salour A. An optimized fault diagnosis method for reciprocating air compressors based on SVM. IEEE International Conference on System Engineering and Technology, 2011: 65-69.
Wang Y, Gao A, Zheng S, Peng X. Experimental investigation of the fault diagnosis of typical faults in reciprocating compressor valves. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science. 2016; 230(13): 2285-2299. https://doi.org/10.1177/095440....
Zhang X, Song Z, Li D, Zhang W, Zhao Z, Chen Y. Fault diagnosis for reducer via improved LMD and SVM-RFE-MRMR. Shock and Vibration 2018: 4526970. https://doi.org/10.1155/2018/4....
Tang X, He Q, Gu X, Li C, Zhang H, Lu J. A Novel bearing fault diagnosis method based on GL-mRMR-SVM. Processes. 2020; 8(7): 784. https://doi.org/10.3390/pr8070....
Zubaidi SL, Ortega-Martorell S, Al-Bugharbee H, Olier I, Hashim KS, Gharghan SK, Kot P, Al-Khaddar R. Urban water demand prediction for a city that suffers from climate change and population growth: Gauteng province case study. Water 2020; 12(7): 1885. https://doi.org/10.3390/w12071....
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