{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T23:44:21Z","timestamp":1784763861503,"version":"3.55.0"},"reference-count":72,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T00:00:00Z","timestamp":1704931200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council","doi-asserted-by":"publisher","award":["ALLRP 549804-19"],"award-info":[{"award-number":["ALLRP 549804-19"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Alberta Electric System Operator, AltaLink, ATCO Electric, ENMAX, EPCOR Inc.","award":["ALLRP 549804-19"],"award-info":[{"award-number":["ALLRP 549804-19"]}]},{"name":"FortisAlberta","award":["ALLRP 549804-19"],"award-info":[{"award-number":["ALLRP 549804-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Due to aging infrastructure, technical issues, increased demand, and environmental developments, the reliability of power systems is of paramount importance. Utility companies aim to provide uninterrupted and efficient power supply to their customers. To achieve this, they focus on implementing techniques and methods to minimize downtime in power networks and reduce maintenance costs. In addition to traditional statistical methods, modern technologies such as machine learning have become increasingly common for enhancing system reliability and customer satisfaction. The primary objective of this study is to review parametric and nonparametric machine learning techniques and their applications in relation to maintenance-related aspects of power distribution system assets, including (1) distribution lines, (2) transformers, and (3) insulators. Compared to other reviews, this study offers a unique perspective on machine learning algorithms and their predictive capabilities in relation to the critical components of power distribution systems.<\/jats:p>","DOI":"10.3390\/info15010037","type":"journal-article","created":{"date-parts":[[2024,1,11]],"date-time":"2024-01-11T03:21:41Z","timestamp":1704943301000},"page":"37","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Parametric and Nonparametric Machine Learning Techniques for Increasing Power System Reliability: A Review"],"prefix":"10.3390","volume":"15","author":[{"given":"Fariha","family":"Imam","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7780-5048","authenticated-orcid":false,"given":"Petr","family":"Musilek","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4783-0717","authenticated-orcid":false,"given":"Marek Z.","family":"Reformat","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada"},{"name":"Information Technology Institute, University of Social Sciences, 90-113 Lodz, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xie, J., Alvarez-Fernandez, I., and Sun, W. (2020, January 2\u20136). A Review of Machine Learning Applications in Power System Resilience. Proceedings of the 2020 IEEE Power & Energy Society General Meeting (PESGM), Montreal, QC, Canada.","DOI":"10.1109\/PESGM41954.2020.9282137"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"113512","DOI":"10.1109\/ACCESS.2020.3003568","article-title":"A Review of Machine Learning Approaches to Power System Security and Stability","volume":"8","author":"Alimi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1007\/s12667-021-00448-6","article-title":"A review of power system protection and asset management with machine learning techniques","volume":"13","author":"Aminifar","year":"2022","journal-title":"Energy Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"109947","DOI":"10.1016\/j.measurement.2021.109947","article-title":"A survey of fault prediction and location methods in electrical energy distribution networks","volume":"184","author":"Dashti","year":"2021","journal-title":"Measurement"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Esmaeili Nezhad, A., and Samimi, M. (2022). A review of the applications of machine learning in the condition monitoring of transformers. Energy Syst., 1\u201331.","DOI":"10.1007\/s12667-022-00532-5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"905","DOI":"10.28991\/ESJ-2022-06-04-017","article-title":"Application of Machine Learning Methods for Asset Management on Power Distribution Networks","volume":"6","author":"Rajora","year":"2020","journal-title":"Emerg. Sci. J."},{"key":"ref_7","unstructured":"Mahmoud, H. (2019). Parametric versus Semi and Nonparametric Regression Models. arXiv."},{"key":"ref_8","first-page":"63","article-title":"Machine Learning Algorithms in Big data Analytics","volume":"6","author":"Bhargavi","year":"2018","journal-title":"Int. J. Comput. Sci. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"James, G., Witten, D., Hastie, T., and Tibshirani, R. (2021). An Introduction to Statistical Learning with Applications in R, Springer.","DOI":"10.1007\/978-1-0716-1418-1"},{"key":"ref_10","unstructured":"Murphy, K.P. (2013). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_11","unstructured":"Haykin, S. (1994). Neural Networks, a Comprehensive Foundation, Prentice-Hall."},{"key":"ref_12","unstructured":"Philipp, G., and Carbonell, J. (2017). Nonparametric Neural Networks. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kankanala, P., Pahwa, A., and Das, S. (2011, January 24\u201328). Regression models for outages due to wind and lightning on overhead distribution feeders. Proceedings of the 2011 IEEE Power and Energy Society General Meeting, Detroit, MI, USA.","DOI":"10.1109\/PES.2011.6039747"},{"key":"ref_14","unstructured":"Zhou, Y., Pahwa, A., and Das, S. (2004, January 12\u201314). Prediction of weather-related failures of overhead distribution feeders. Proceedings of the 2004 International Conference on Probabilistic Methods Applied to Power Systems, Ames, IA, USA."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1109\/TPWRD.2005.848734","article-title":"Statistical evaluation of lightning overvoltage\u2019s on overhead distribution lines using neural networks","volume":"20","author":"Martinez","year":"2005","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sarajcev, P. (2022, January 5\u20138). Bagging Ensemble Classifier for Predicting Lightning Flashovers on Distribution Lines. Proceedings of the 2022 7th International Conference on Smart and Sustainable Technologies (SpliTech), Split\/Bol, Croatia.","DOI":"10.23919\/SpliTech55088.2022.9854317"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/TPWRD.2002.804006","article-title":"Predicting vegetation-related failure rates for overhead distribution feeders","volume":"17","author":"Radmer","year":"2002","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Melagoda, A.U., Karunarathna, T.D.L.P., Nisaharan, G., Amarasinghe, P.A.G.M., and Abeygunawardane, S.K. (2021, January 2\u20133). Application of Machine Learning Algorithms for Predicting Vegetation Related Outages in Power Distribution Systems. Proceedings of the 2021 3rd International Conference on Electrical Engineering (EECon), Colombo, Sri Lanka.","DOI":"10.1109\/EECon52960.2021.9580947"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"270","DOI":"10.1016\/j.ifacol.2015.12.389","article-title":"Estimating animal-related outages on overhead distribution feeders using boosting","volume":"48","author":"Kankanala","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1007\/s00202-016-0428-8","article-title":"Artificial neural-network-based fault location for power distribution lines using the frequency spectra of fault data","volume":"99","author":"Aslan","year":"2017","journal-title":"Electr. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"497","DOI":"10.1016\/j.ijepes.2006.11.009","article-title":"Application of wavelet fuzzy neural network in locating single line to ground fault (SLG) in distribution lines","volume":"29","author":"Chunju","year":"2007","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1927","DOI":"10.1109\/61.473361","article-title":"On the application of a machine learning technique to fault diagnosis of power distribution lines","volume":"10","author":"Togami","year":"1995","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Min, F., Yaling, L., Xi, Z., Huan, C., Yaqian, H., Libo, F., and Qing, Y. (2019, January 1\u20133). Fault prediction for distribution network based on CNN and LightGBM algorithm. Proceedings of the 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Changsha, China.","DOI":"10.1109\/ICEMI46757.2019.9101423"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Ngaopitakkul, A., Pothisarn, C., Bunjongjit, S., and Suechoey, B. (2012, January 26\u201328). An Application of Discrete Wavelet Transform and Support Vector Machines Algorithm for Classification of Fault Types on Underground Cable. Proceedings of the 2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications, Kaohsiung, Taiwan.","DOI":"10.1109\/IBICA.2012.21"},{"key":"ref_25","unstructured":"Apisit, C., and Ngaopitakkul, A. (2010, January 17\u201319). Identification of Fault Types for Underground Cable using Discrete Wavelet Transform. Proceedings of the International MultiConference of Engineers and Computer Scientists, Hong Kong, China."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Oliveira, A., Leit\u00e3o, A., Carvalho, L., Dias, L., Guimar\u00e3es, L., and Ribeiro, M. (2021, January 21\u201323). Data-driven methodology to predict distribution lines failure location. Proceedings of the CIRED 2021\u2014The 26th International Conference and Exhibition on Electricity Distribution, online.","DOI":"10.1049\/icp.2021.1830"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Prasad, P.S., and Rao, B.P. (2016, January 23\u201326). LBP-HF features and machine learning applied for automated monitoring of insulators for overhead power distribution lines. Proceedings of the 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, India.","DOI":"10.1109\/WiSPNET.2016.7566245"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1109\/TDEI.2011.5739465","article-title":"A DOST based approach for the condition monitoring of 11 kV distribution line insulators","volume":"18","author":"Chandra","year":"2011","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TDEI.2013.6508770","article-title":"Condition monitoring of 11 kV distribution system insulators incorporating complex imagery using combined DOST-SVM approach","volume":"20","author":"Reddy","year":"2013","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1109\/TDEI.2010.5412006","article-title":"Insulator condition analysis for overhead distribution lines using combined wavelet support vector machine (SVM)","volume":"17","author":"Murthy","year":"2010","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"135","DOI":"10.25103\/jestr.095.21","article-title":"Review on Machine Vision based Insulator Inspection Systems for Power Distribution System","volume":"9","author":"Prasad","year":"2016","journal-title":"J. Eng. Sci. Technol. Rev."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TIM.2019.2956300","article-title":"Application of Machine Learning to Evaluate Insulator Surface Erosion","volume":"69","author":"Ibrahim","year":"2020","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_33","unstructured":"(2022). Electrical Insulating Materials Used under Severe Ambient Conditions\u2014Test Methods for Evaluating Resistance to Tracking and Erosion, Edition 4. Standard No. IEC 60587:2022."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1591","DOI":"10.1049\/iet-gtd.2019.1579","article-title":"Analysis of training techniques of ANN for classification of insulators in electrical power systems","volume":"14","author":"Stefenon","year":"2020","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Sopelsa Neto, N.F., Stefenon, S.F., Meyer, L.H., Bruns, R., Nied, A., Seman, L.O., Gonzalez, G.V., Leithardt, V.R.Q., and Yow, K.-C. (2021). A Study of Multilayer Perceptron Networks Applied to Classification of Ceramic Insulators Using Ultrasound. Appl. Sci., 11.","DOI":"10.3390\/app11041592"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Frizzo Stefenon, S., Zanetti Freire, R., dos Santos Coelho, L., Meyer, L.H., Bartnik Grebogi, R., Gouv\u00eaa Buratto, W., and Nied, A. (2020). Electrical Insulator Fault Forecasting Based on a Wavelet Neuro-Fuzzy System. Energies, 13.","DOI":"10.3390\/en13020484"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Aghay Kaboli, S.H., Al Hinai, A., Al-Badi, A., Charabi, Y., and Al Saifi, A. (2019). Prediction of Metallic Conductor Voltage Owing to Electromagnetic Coupling Via a Hybrid ANFIS and Backtracking Search Algorithm. Energies, 12.","DOI":"10.3390\/en12193651"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"686","DOI":"10.1109\/TDEI.2017.006936","article-title":"Bayesian regularization of neural network to predict leakage current in a salt fog environment","volume":"25","author":"Khafaf","year":"2018","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Dey, U., Chandra, M., and Das, S. (2022, January 27\u201329). Insulator Contamination Diagnosis using Unsupervised Machine Learning. Proceedings of the 2022 3rd International Conference for Emerging Technology (INCET), Belgaum, India.","DOI":"10.1109\/INCET54531.2022.9825221"},{"key":"ref_40","unstructured":"Abubakar Mas\u2019ud, A., Stewart, B.G., McMeekin, S.G., and Nesbitt, A. (September, January 31). An ensemble Neural Network for recognizing PD patterns. Proceedings of the 45th International Universities Power Engineering Conference UPEC2010, Cardiff, UK."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Mas\u2019ud, A.A., Albarrac\u00edn, R., Ardila-Rey, J.A., Muhammad-Sukki, F., Illias, H.A., Bani, N.A., and Munir, A.B. (2016). Artificial Neural Network Application for Partial Discharge Recognition: Survey and Future Directions. Energies, 9.","DOI":"10.3390\/en9080574"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Corso, M.P., Perez, F.L., Stefenon, S.F., Yow, K.-C., Garc\u00eda Ovejero, R., and Leithardt, V.R.Q. (2021). Classification of Contaminated Insulators Using k-Nearest Neighbors Based on Computer Vision. Computers, 10.","DOI":"10.20944\/preprints202108.0282.v1"},{"key":"ref_43","first-page":"1","article-title":"Transformer Failure Analysis: Reasons and Methods","volume":"4","author":"Maan","year":"2016","journal-title":"Int. J. Eng. Res. Technol."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Youssef, M.M., Ibrahim, R.A., Desouki, H., and Moustafa, M.M.Z. (2022, January 4\u20136). An Overview on Condition Monitoring & Health Assessment Techniques for Distribution Transformers. Proceedings of the 2022 6th International Conference on Green Energy and Applications (ICGEA), Singapore.","DOI":"10.1109\/ICGEA54406.2022.9791900"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TDEI.2012.6215106","article-title":"A new approach to identify power transformer criticality and asset management decision based on dissolved gas-in-oil analysis","volume":"19","author":"Islam","year":"2012","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_46","first-page":"423","article-title":"Comparative study on three kinds of transformer fault diagnosis method","volume":"10","author":"Qiming","year":"2006","journal-title":"Power Syst. Technol."},{"key":"ref_47","first-page":"66","article-title":"Four ratio method in the application of the transformer overheating fault judgment","volume":"48","author":"Jian","year":"2011","journal-title":"Transformer"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1108\/JQME-06-2021-0052","article-title":"Machine learning for predictive maintenance scheduling of distribution transformers","volume":"29","year":"2023","journal-title":"J. Qual. Maint. Eng."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Bravo, M.D.A., Lozano, C., and Alvarez, L. (2021). Dataset of Distribution Transformers at Cauca Department (Colombia). Mendeley Data, 4.","DOI":"10.1016\/j.dib.2021.107454"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Kabir, F., Foggo, B., and Yu, N. (2018, January 17\u201319). Data Driven Predictive Maintenance of Distribution Transformers. Proceedings of the 2018 China International Conference on Electricity Distribution (CICED), Tianjin, China.","DOI":"10.1109\/CICED.2018.8592417"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Qu, L., and Zhou, H. (2015, January 18\u201324). The Multi-class SVM Is Applied in Transformer Fault Diagnosis. Proceedings of the 2015 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES), Guiyang, China.","DOI":"10.1109\/DCABES.2015.125"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Sherpa, K.S., Bhoi, A.K., Kalam, A., and Mishra, M.K. (2021). Advances in Smart Grid and Renewable Energy, Proceedings of the ETAEERE 2020, Bhubaneswar, India, 5\u20136 March 2021, Springer. Lecture Notes in Electrical Engineering.","DOI":"10.1007\/978-981-15-7511-2"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1836","DOI":"10.1109\/61.544265","article-title":"An artificial neural network approach to transformer fault diagnosis","volume":"11","author":"Zhang","year":"1996","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Dong, H., Yang, X., and Li, A. (2018, January 15\u201317). A Novel Method for Power Transformer Fault Diagnosis Based on Bat-BP Algorithm. Proceedings of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi\u2019an, China.","DOI":"10.1109\/SDPC.2018.8664751"},{"key":"ref_55","first-page":"65","article-title":"A new metaheuristic bat-inspired algorithm, Nature Inspired Cooperative Strategies for Optimization (NICSO 2010)","volume":"Volume 284","author":"Pelta","year":"2010","journal-title":"Studies in Computational Intelligence"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"631","DOI":"10.1109\/61.956749","article-title":"Diagnosing failed distribution transformers using neural networks","volume":"16","author":"Farag","year":"2001","journal-title":"IEEE Trans. Power Deliv."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chen, X., Cui, H., and Luo, L. (2011, January 28\u201329). Fault Diagnosis of Transformer Based on Random Forest. Proceedings of the 2011 Fourth International Conference on Intelligent Computation Technology and Automation, Shenzhen, China.","DOI":"10.1109\/ICICTA.2011.40"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"012133","DOI":"10.1088\/1757-899X\/1055\/1\/012133","article-title":"Fault Diagnosis and Asset Management of Power Transformer Using Adaptive Boost Machine Learning Algorithm","volume":"Volume 1055","author":"Balaraman","year":"2021","journal-title":"IOP Conference Series: Materials Science and Engineering"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Balan, A., Srujan, T.L., Manitha, P.V., and Deepa, K. (2023, January 5\u20137). Detection and Analysis of Faults in Transformer using Machine Learning. Proceedings of the 2023 International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT), Bengaluru, India.","DOI":"10.1109\/IDCIoT56793.2023.10052786"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Abu-Elanien, A.E.B., Salama, M.M.A., and Ibrahim, M. (2011, January 5\u201318). Determination of transformer health condition using artificial neural networks. Proceedings of the 2011 International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey.","DOI":"10.1109\/INISTA.2011.5946173"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jaiswal, G.C., Ballal, M.S., and Tutakne, D.R. (2017, January 21\u201323). ANN based methodology for determination of distribution transformer health status. Proceedings of the 2017 7th International Conference on Power Systems (ICPS), Shivajinagar, India.","DOI":"10.1109\/ICPES.2017.8387281"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Duarte, L.J., Pinheiro, A.P., and Ferreira, D.O. (2022). A Real-Time Method to Estimate the Operational Condition of Distribution Transformers. Energies, 15.","DOI":"10.3390\/en15228716"},{"key":"ref_63","first-page":"1","article-title":"Machine learning for assessing the service transformer health using an energy monitor device","volume":"15","author":"Quynh","year":"2020","journal-title":"IOSR J. Electr. Electron. Eng."},{"key":"ref_64","first-page":"536","article-title":"A Hybrid Machine Learning Approach for Predictive Maintenance in Smart Factories of the Future","volume":"Volume 536","author":"Moon","year":"2018","journal-title":"Advances in Production Management Systems. Smart Manufacturing for Industry 4.0, Proceedings of the APMS 2018 IFIP Advances in Information and Communication Technology, Seoul, Republic of Korea, 26\u201330 August 2018"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Xie, S., Xue, F., Zhang, W., and Zhu, J. (2023). Data-Driven Predictive Maintenance Policy Based on Dynamic Probability Distribution Prediction of Remaining Useful Life. Machines, 11.","DOI":"10.3390\/machines11100923"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"110276","DOI":"10.1016\/j.measurement.2021.110276","article-title":"Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective","volume":"187","author":"Wen","year":"2022","journal-title":"Measurement"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Cinar, E. (2022). A Sensor Fusion Method Using Transfer Learning Models for Equipment Condition Monitoring. Sensors, 22.","DOI":"10.3390\/s22186791"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"49355","DOI":"10.1109\/ACCESS.2021.3069137","article-title":"Mcardle, Edge Intelligence for Data Handling and Predictive Maintenance in IIOT","volume":"9","author":"THafeez","year":"2021","journal-title":"IEEE Access"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"e14534","DOI":"10.1016\/j.heliyon.2023.e14534","article-title":"Overview of predictive maintenance based on digital twin technology","volume":"9","author":"Zhong","year":"2023","journal-title":"Heliyon"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Qiu, S., Cui, X., Ping, Z., Shan, N., Li, Z., Bao, X., and Xu, X. (2023). Deep Learning Techniques in Intelligent Fault Diagnosis and Prognosis for Industrial Systems: A Review. Sensors, 23.","DOI":"10.3390\/s23031305"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"108521","DOI":"10.1016\/j.cie.2022.108521","article-title":"Deep transfer learning for failure prediction across failure types","volume":"172","author":"Li","year":"2022","journal-title":"Comput. Ind. Eng."},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Walker, C.M., Agarwal, W., Lin, L., Hall, A.C., Hill, R.A., Boring, R.L., Mortenson, T.J., and Lybeck, N.J. (2023). Explainable Artificial Intelligence Technology for Predictive Maintenance.","DOI":"10.2172\/1998555"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/1\/37\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T13:44:20Z","timestamp":1760103860000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/1\/37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,11]]},"references-count":72,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["info15010037"],"URL":"https:\/\/doi.org\/10.3390\/info15010037","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,11]]}}}