PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE

Jakub Gęca

j.geca@pollub.pl
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Lublin, Poland (Poland)
https://orcid.org/0000-0003-0610-2975

Abstract

The consequences of failures and unscheduled maintenance are the reasons why engineers have been trying to increase the reliability of industrial equipment for years. In modern solutions, predictive maintenance is a frequently used method. It allows to forecast failures and alert about their possibility. This paper presents a summary of the machine learning algorithms that can be used in predictive maintenance and comparison of their performance. The analysis was made on the basis of data set from Microsoft Azure AI Gallery. The paper presents a comprehensive approach to the issue including feature engineering, preprocessing, dimensionality reduction techniques, as well as tuning of model parameters in order to obtain the highest possible performance. The conducted research allowed to conclude that in the analysed case , the best algorithm achieved 99.92% accuracy out of over 122 thousand test data records. In conclusion, predictive maintenance based on machine learning represents the future of machine reliability in industry.


Keywords:

machine learning, random forest, predictive maintenance, neural networks

Binding A., et al.: Machine Learning Predictive Maintenance on Data in the Wild. IEEE 5th World Forum on Internet of Things (Wf-Iot), 2019, 507–512.
DOI: https://doi.org/10.1109/WF-IoT.2019.8767312   Google Scholar

Burnaev E.: On Construction of Early Warning Systems for Predictive Maintenance in Aerospace Industry. Journal of Communications Technology and Electronics 64/2019, 1473–1484, [https://doi.org/10.1134/S1064226919120027].
DOI: https://doi.org/10.1134/S1064226919120027   Google Scholar

Campos J. R., et al.: Exploratory Study of Machine Learning Techniques for Supporting Failure Prediction. 14th European Dependable Computing Conference (EDCC), 2018, 9–16, [https://doi.org/10.1109/EDCC.2018.00014].
DOI: https://doi.org/10.1109/EDCC.2018.00014   Google Scholar

Carvalho T.P., et al.: A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance. Computers & Industrial Engineering 137/2019, 106024, [https://doi.org/10.1016/j.cie.2019.106024].
DOI: https://doi.org/10.1016/j.cie.2019.106024   Google Scholar

Chigurupati A., et al.: Predicting Hardware Failure Using Machine Learning. 2016 Annual Reliability and Maintainability Symposium (RAMS), 2016, 1–6, [https://doi.org/10.1109/RAMS.2016.7448033].
DOI: https://doi.org/10.1109/RAMS.2016.7448033   Google Scholar

Cho S., et al.: A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future. Advances in Production Management Systems: Smart Manufacturing for Industry 4.0 – APMS 2018, 536/2018, 311–317, [https://doi.org/10.1007/978-3-319-99707-0_39].
DOI: https://doi.org/10.1007/978-3-319-99707-0_39   Google Scholar

Corazza A., et al.: A Machine Learning Approach for Predictive Maintenance for Mobile Phones Service Providers. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 1/2017, 717–726, [https://doi.org/10.1007/978-3-319-49109–7_69].
DOI: https://doi.org/10.1007/978-3-319-49109-7_69   Google Scholar

Dzierżak R.: Comparison of the Influence of Standardization and Normalization of Data on the Effectiveness of Spongy Tissue Texture Classification. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9/2019, 66–69, [https://doi.org/10.35784/iapgos.62].
DOI: https://doi.org/10.35784/iapgos.62   Google Scholar

Garcia S., et al.: Data Preprocessing in Data Mining. Data Preprocessing in Data Mining 72/2015, 1–320, [https://doi.org/10.1007/978-3-319-10247-4].
DOI: https://doi.org/10.1007/978-3-319-10247-4   Google Scholar

Gutschi C., et al.: Log-Based Predictive Maintenance in Discrete Parts Manufacturing. 12th Cirp Conference on Intelligent Computation in Manufacturing Eng. 79/2019, 528–533,[https://doi.org/10.1016/j.procir.2019.02.098].
DOI: https://doi.org/10.1016/j.procir.2019.02.098   Google Scholar

Jiang R., et al.: Failure Prediction Method of Gearbox Based on Bp Neural Network with Genetic Optimization Algorithm. International Conference on Renewable Power Generation – RPG 2015, 2015, 1–3, [https://doi.org/10.1049/cp.2015.0444].
DOI: https://doi.org/10.1049/cp.2015.0444   Google Scholar

Kanawaday A., Sane A.: Machine Learning for Predictive Maintenance of Industrial Machines Using Iot Sensor Data. 2017, 87–90, [https://doi.org/10.1109/ICSESS.2017.8342870].
DOI: https://doi.org/10.1109/ICSESS.2017.8342870   Google Scholar

Khalil M.: Failure Prediction of Pv Inverters under Operational Stresses. IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), 2019, 1–5, [https://doi.org/10.1109/EEEIC.2019.8783241].
DOI: https://doi.org/10.1109/EEEIC.2019.8783241   Google Scholar

Kolokas N., et al.: Forecasting Faults of Industrial Equipment Using Machine Learning Classifiers. 2018 Innovations in Intelligent Systems and Applications (Inista), 2018, 6.
  Google Scholar

Korvesis P., et al.: Predictive Maintenance in Aviation: Failure Prediction from Post-Flight Reports. IEEE 34th International Conference on Data Engineering (ICDE), 2018, 1414–1422, [https://doi.org/10.1109/ICDE.2018.00160].
DOI: https://doi.org/10.1109/ICDE.2018.00160   Google Scholar

Lemaître G., Nogueira F., Aridas C.: Imbalanced-Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. 18/2016.
  Google Scholar

Masani K.I., et al.: Predictive Maintenance and Monitoring of Industrial Machine Using Machine Learning. Scalable Computing-Practice and Experience 20(4)/2019, 663–668, [https://doi.org/10.12694/scpe.v20i4.1585].
DOI: https://doi.org/10.12694/scpe.v20i4.1585   Google Scholar

Mishra K., et al.: Failure Prediction Model for Predictive Maintenance. 7th IEEE International Conference on Cloud Computing in Emerging Markets (CCEM), 2018, 72–75, [https://doi.org/10.1109/ccem.2018.00019].
DOI: https://doi.org/10.1109/CCEM.2018.00019   Google Scholar

Parisi L., Ravi Chandran N.: Genetic Algorithms and Unsupervised Machine Learning for Predicting Robotic Manipulation Failures for Force-Sensitive Tasks. 4th International Conference on Control, Automation and Robotics (ICCAR), 2018, 22–25, [https://doi.org/10.1109/ICCAR.2018.8384638].
DOI: https://doi.org/10.1109/ICCAR.2018.8384638   Google Scholar

Rosenblatt F.: The Perceptron, a Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory, 1957. Report: Cornell Aeronautical Laboratory.
  Google Scholar

Rymarczyk T., et al.: Analysis of Data from Measuring Sensors for Prediction in Production Process Control Systems. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9(4)/2019, [https://doi.org/10.35784/iapgos.570].
DOI: https://doi.org/10.35784/iapgos.570   Google Scholar

Schaub M.: Data-Based Prediction of Soot Emissions for Transient Engine Operation. Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9(4)/2019, [https://doi.org/10.35784/iapgos.29].
DOI: https://doi.org/10.35784/iapgos.29   Google Scholar

Suchatpong T., Bhumkittipich K.: Hard Disk Drive Failure Mode Prediction Based on Industrial Standard Using Decision Tree Learning. 11th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), 2014, 1–4, [https://doi.org/10.1109/ECTICon.2014.6839839].
DOI: https://doi.org/10.1109/ECTICon.2014.6839839   Google Scholar

Susto G.A., et al.: Machine Learning for Predictive Maintenance: A Multiple Classifier Approach. IEEE Transactions on Industrial Informatics 11(3)/2015, 812–820, [https://doi.org/10.1109/TII.2014.2349359].
DOI: https://doi.org/10.1109/TII.2014.2349359   Google Scholar

https://gallery.azure.ai/Experiment/Predictive-Maintenance-Implementation-Guide-Data-Sets-1 (available: 24.04.2020).
  Google Scholar

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Published
2020-09-30

Cited by

Gęca, J. (2020). PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 10(3), 32–35. https://doi.org/10.35784/iapgos.1834

Authors

Jakub Gęca 
j.geca@pollub.pl
Lublin University of Technology, Faculty of Electrical Engineering and Computer Science, Lublin, Poland Poland
https://orcid.org/0000-0003-0610-2975

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