PERFORMANCE COMPARISON OF MACHINE LEARNING ALGORITHMS FOR PREDICTIVE MAINTENANCE
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.
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.
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].
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].
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].
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].
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].
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].
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].
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].
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].
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].
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].
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].
Kolokas N., et al.: Forecasting Faults of Industrial Equipment Using Machine Learning Classifiers. 2018 Innovations in Intelligent Systems and Applications (Inista), 2018, 6.
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].
Lemaître G., Nogueira F., Aridas C.: Imbalanced-Learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning. 18/2016.
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].
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].
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].
Rosenblatt F.: The Perceptron, a Perceiving and Recognizing Automaton Project Para. Cornell Aeronautical Laboratory, 1957. Report: Cornell Aeronautical Laboratory.
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].
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].
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].
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].
https://gallery.azure.ai/Experiment/Predictive-Maintenance-Implementation-Guide-Data-Sets-1 (available: 24.04.2020).
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