PORÓWNANIE SKUTECZNOŚCI ALGORYTMÓW UCZENIA MASZYNOWEGO DLA KONSERWACJI PREDYKCYJNEJ

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