Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions
This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by pr...
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| Format: | Article |
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MDPI AG
2025-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/9/4898 |
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| author | Christos Tsallis Panagiotis Papageorgas Dimitrios Piromalis Radu Adrian Munteanu |
| author_facet | Christos Tsallis Panagiotis Papageorgas Dimitrios Piromalis Radu Adrian Munteanu |
| author_sort | Christos Tsallis |
| collection | DOAJ |
| description | This systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by predicting faults, estimating remaining useful life (RUL), and reducing operational downtime. Sixty peer-reviewed articles published between 2020 and 2024 were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and were analyzed based on industrial sector, ML techniques, datasets, evaluation metrics, and implementation challenges. Results show that combining ML with diverse sensor data enhances predictive performance under varying operational conditions across manufacturing, energy, healthcare, and transportation. Frequently used open datasets include the commercial modular aero-propulsion system simulation (CMAPSS), the malfunctioning industrial machine investigation and inspection (MIMII), and the semiconductor manufacturing process (SECOM) datasets, though data heterogeneity and imbalance remain major barriers. Emerging paradigms such as hybrid modeling, digital twins, and physics-informed learning show promise but face issues like computational cost, interpretability, and limited scalability. The findings highlight future research needs in model generalizability, real-world validation, and explainable artificial intelligence (AI) to bridge gaps between ML innovations and industrial practice. |
| format | Article |
| id | doaj-art-3fd3fb057bb14cf89c629b91cf900a2a |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3fd3fb057bb14cf89c629b91cf900a2a2025-08-20T02:24:47ZengMDPI AGApplied Sciences2076-34172025-04-01159489810.3390/app15094898Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future DirectionsChristos Tsallis0Panagiotis Papageorgas1Dimitrios Piromalis2Radu Adrian Munteanu3Department of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, GreeceDepartment of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, GreeceDepartment of Electrical and Electronics Engineering, University of West Attica, 12244 Athens, GreeceElectrotechnics and Measurement Department, Technical University of Cluj-Napoca, 400114 Cluj-Napoca, RomaniaThis systematic literature review (SLR) provides a comprehensive application-wise analysis of machine learning (ML)-driven predictive maintenance (PdM) across industrial domains. Motivated by the digital transformation of industry 4.0, this study explores how ML techniques optimize maintenance by predicting faults, estimating remaining useful life (RUL), and reducing operational downtime. Sixty peer-reviewed articles published between 2020 and 2024 were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) 2020 guidelines, and were analyzed based on industrial sector, ML techniques, datasets, evaluation metrics, and implementation challenges. Results show that combining ML with diverse sensor data enhances predictive performance under varying operational conditions across manufacturing, energy, healthcare, and transportation. Frequently used open datasets include the commercial modular aero-propulsion system simulation (CMAPSS), the malfunctioning industrial machine investigation and inspection (MIMII), and the semiconductor manufacturing process (SECOM) datasets, though data heterogeneity and imbalance remain major barriers. Emerging paradigms such as hybrid modeling, digital twins, and physics-informed learning show promise but face issues like computational cost, interpretability, and limited scalability. The findings highlight future research needs in model generalizability, real-world validation, and explainable artificial intelligence (AI) to bridge gaps between ML innovations and industrial practice.https://www.mdpi.com/2076-3417/15/9/4898machine learningpredictive maintenancedigital twinsInternet of ThingsIndustry 4.0fault diagnosis |
| spellingShingle | Christos Tsallis Panagiotis Papageorgas Dimitrios Piromalis Radu Adrian Munteanu Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions Applied Sciences machine learning predictive maintenance digital twins Internet of Things Industry 4.0 fault diagnosis |
| title | Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions |
| title_full | Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions |
| title_fullStr | Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions |
| title_full_unstemmed | Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions |
| title_short | Application-Wise Review of Machine Learning-Based Predictive Maintenance: Trends, Challenges, and Future Directions |
| title_sort | application wise review of machine learning based predictive maintenance trends challenges and future directions |
| topic | machine learning predictive maintenance digital twins Internet of Things Industry 4.0 fault diagnosis |
| url | https://www.mdpi.com/2076-3417/15/9/4898 |
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