Abstract
Predictive maintenance refers to predicting malfunctions using data from monitoring equipment and process performance measurements. Machine learning algorithms and techniques are often used to analyze equipment monitoring data. Machine learning is the process in which a computer can work more precisely by collecting and analyzing data. It is often the case that machine learning algorithms use supervised learning, in which labelled data is used to feed the algorithm. However, there are many supervised machine learning algorithms available. Therefore, choosing the best-supervised machine learning algorithm to resolve predictive maintenance issues is not trivial.
This paper aims to increase the performance of predictive maintenance and achieve its goals by selecting the most suitable supervised machine learning algorithm.
Based on the most commonly used criteria in research articles, we selected three supervised machine learning algorithms from a comparative study: Random forest, Decision tree and KNN. We then tested selected algorithms on data from real-world and simulation scenarios. Finally, we conducted the experiment based on vibration analysis and reliability evaluation. We noticed that Random forests and Decision trees obtained slightly the same performance. KNN is a better classification algorithm for extensive volumes of data; on the contrary, Random forest performs better in the case of small datasets.
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Availability of data and material
Data used for simulation has been downloaded from: https://www.kaggle.com/
Code availability
The source code is freely available at: https://github.com/fettah140/regression_classification
Notes
The PHD is a software that enables the collection, storage, and analysis of process data. It provides users with tools to convert process data into valuable information that can be used to detect mistakes and improve processes.
References
Abdallah K (2007) Techniques de Maintenance Prédictive pour l’Amélioration de la disponibilité des Installations, Doctoral dissertation. Université de Annaba-Badji Mokhtar
Alsharif MH, Kelechi AH, Yahya K, Chaudhry SA (2020) Machine learning algorithms for smart data analysis in the Internet of things environment: taxonomies and research trends. Symmetry 12(1):88
Asad A (2016) Three types of Machine Learning Algorithms
Baptista M, Sankararaman S, de Medeiros IP, Nascimento C Jr, Prendinger H, Henriques EM (2018) Forecasting fault events for predictive maintenance using data-driven techniques and ARMA modelling. Comput Ind Eng 115:41–53
Benchettouh SE (2019) Elaboration d’un système de prédiction des pannes et de planification des maintenances. Doctoral dissertation, universite mohamed boudiaf-m’sila-faculte mathematiques et de l’informatique
Bensana T (2008) Diagnistic des défaillances basé sur l'analyse vibratoire d'une turbine à vapeur. Doctoral dissertation, Université de Annaba-Badji Mokhtar
Bishop CM (1995) Neural networks for pattern recognition. Oxford University Press
Boukili A, El Hammoumi M (2015) Preventive maintenance by vibratory analysis: case study. Int J Eng Res 1:450–455
Carvalho TP, Soares FA, Vita R, Francisco RD, Basto JP, Alcalá SG (2019) A systematic literature review of machine learning methods applied to predictive maintenance. Comput Ind Eng 137:106024
Cielen D, Meysman AD, Ali M (2016) Introducing data science: big data, machine learning, and more, using Python tools. Manning Publications Co.
Çınar ZM, Abdussalam Nuhu A, Zeeshan Q, Korhan O, Asmael M, Safaei B (2020) Machine learning in predictive maintenance towards sustainable smart manufacturing in industry 4.0. Sustainability 12(19):8211
Dey A (2016) Machine learning algorithms: a review. International Journal of Computer Science and Information Technologies 7(3):1174–1179
Djebili O (2013) Contribution à la maintenance prédictive par analyse vibratoire des composants mécaniques tournants. Application aux butées à billes soumises à la fatigue de contact de roulement (Doctoral dissertation, Reims)
He Y, Gu C, Chen Z, Han X (2017) Integrated predictive maintenance strategy for manufacturing systems by combining quality control and mission reliability analysis. Int J Prod Res 55(19):5841–5862
Hormozi H, Hormozi E, Nohooji HR (2012) The classification of the applicable machine learning methods in robot manipulators. Int J Mach 2(5):560
Jardine AK, Lin D, Banjevic D (2006) A review on machinery diagnostics and prognostics implementing condition-based maintenance. Mech Syst Signal Process 20(7):1483–1510
Jensen FV (1996) An introduction to Bayesian networks. London: UCL Press. 210:1–178
Kebabsa T, Ouelaa N, Djebala A (2018) Experimental vibratory analysis of a fan motor in industrial environment. Int J Adv Manuf Syst 98(9):2439–2447
Kumar AR, Krishnan V (2017) A study on system reliability in Weibull distribution. Methods 5(3):23–28
Lee D, Pan R (2017) Predictive maintenance of the complex system with multi-level reliability structure. Int J Prod Res 55(16):4785–4801
Motaghare O, Pillai AS, Ramachandran KI (2018) Predictive maintenance architecture. In 2018 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC) pp. 1–4
Nasir T, Asmaela M, Zeeshana Q, Solyalib D (2020) Applications of machine learning to friction stir welding process optimization. Jurnal Kejuruteraan 32(1):171–186
Nasteski V (2017) An overview of the supervised machine learning methods. Horizons b 4:51–62
Osisanwo FY, Akinsola JET, Awodele O, Hinmikaiye JO, Olakanmi O, Akinjobi J (2017) Supervised machine learning algorithms: classification and comparison. International Journal of Computer Trends and Technology (IJCTT) 48(3):128–138
Rokach L, Maimon O (2005) Top-down induction of decision trees classifiers-a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 35(4):476–487
Singh A, Thakur N, Sharma A (2016) A review of supervised machine learning algorithms. In Proceedings of the 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom) pp. 1310–1315
Taleb M, Chaib R (2016) Vibration analysis of rotating machines for optimal preventive maintenance. Min Sci 23:191–202
Thomas M (2012) Fiabilité, maintenance prédictive et vibration des machines. PUQ
Wuest T, Weimer D, Irgens C, Thoben KD (2016) Machine learning in manufacturing: advantages, challenges, and applications. Prod Manuf Res 4(1):23–45
Zhou X, Xi L, Lee J (2007) Reliability-centred predictive maintenance scheduling for a continuously monitored system subject to degradation. Reliab Eng Syst Saf 92(4):530–534
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Ouadah, A., Zemmouchi-Ghomari, L. & Salhi, N. Selecting an appropriate supervised machine learning algorithm for predictive maintenance. Int J Adv Manuf Technol 119, 4277–4301 (2022). https://doi.org/10.1007/s00170-021-08551-9
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DOI: https://doi.org/10.1007/s00170-021-08551-9