A Survey on Data Mining for Data-Driven Industrial Assets Maintenance
This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along wit...
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MDPI AG
2025-02-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/13/2/67 |
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| author | Eduardo Coronel Benjamín Barán Pedro Gardel |
| author_facet | Eduardo Coronel Benjamín Barán Pedro Gardel |
| author_sort | Eduardo Coronel |
| collection | DOAJ |
| description | This survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks. |
| format | Article |
| id | doaj-art-6c59829ec7e2465da96abeb87aa89e2f |
| institution | DOAJ |
| issn | 2227-7080 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj-art-6c59829ec7e2465da96abeb87aa89e2f2025-08-20T02:45:30ZengMDPI AGTechnologies2227-70802025-02-011326710.3390/technologies13020067A Survey on Data Mining for Data-Driven Industrial Assets MaintenanceEduardo Coronel0Benjamín Barán1Pedro Gardel2Facultad Politécnica, Universidad Nacional de Asunción, Asunción 2160, ParaguayFacultad de Tecnología y Ciencia Aplicada, Universidad Comunera, Asunción 1412, ParaguayFacultad de Ciencias y Tecnología, Universidad Católica Ntra. Sra. de la Asunción, Campus Alto Paraná, Hernandarias 7220, ParaguayThis survey presents a comprehensive review of data-driven approaches for industrial asset maintenance, emphasizing the use of data mining and machine learning techniques, including deep learning, for condition-based and predictive maintenance. It examines 534 references from 1995 to 2023, along with three additional articles from 2024 on natural language processing and large language models in industrial maintenance. The study categorizes two main techniques, four specialized approaches, and 27 methodologies, resulting in over 100 variations of algorithms tailored to specific maintenance needs for industrial assets. It details the data types utilized in the industrial sector, with the most frequently mentioned being time series data, event timestamp data, and image data. The survey also highlights the most frequently referenced data mining algorithms, such as the proportional hazard model, expert systems, support vector machines, random forest, autoencoder, and convolutional neural networks. Additionally, the survey proposes four level classes of asset complexity and studies five asset types, including mechanical, electromechanical, electrical, electronic, and computing assets. The growing adoption of deep learning is highlighted alongside the continued relevance of traditional approaches such as shallow machine learning and rule-based and model-based techniques. Furthermore, the survey explores emerging trends in machine learning and related technologies, identifies future research directions, and underscores their critical role in advancing condition-based and predictive maintenance frameworks.https://www.mdpi.com/2227-7080/13/2/67data miningmachine learningdeep learningcondition-based maintenancepredictive maintenanceindustrial assets |
| spellingShingle | Eduardo Coronel Benjamín Barán Pedro Gardel A Survey on Data Mining for Data-Driven Industrial Assets Maintenance Technologies data mining machine learning deep learning condition-based maintenance predictive maintenance industrial assets |
| title | A Survey on Data Mining for Data-Driven Industrial Assets Maintenance |
| title_full | A Survey on Data Mining for Data-Driven Industrial Assets Maintenance |
| title_fullStr | A Survey on Data Mining for Data-Driven Industrial Assets Maintenance |
| title_full_unstemmed | A Survey on Data Mining for Data-Driven Industrial Assets Maintenance |
| title_short | A Survey on Data Mining for Data-Driven Industrial Assets Maintenance |
| title_sort | survey on data mining for data driven industrial assets maintenance |
| topic | data mining machine learning deep learning condition-based maintenance predictive maintenance industrial assets |
| url | https://www.mdpi.com/2227-7080/13/2/67 |
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