Artificial intelligence and edge computing for machine maintenance-review
Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data co...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2024
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| Online Access: | http://umpir.ump.edu.my/id/eprint/44109/ http://umpir.ump.edu.my/id/eprint/44109/1/Artificial%20intelligence%20and%20edge%20computing%20for%20machine%20maintenance-review.pdf |
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| author | Bala, Abubakar Rahimi Zaman, Jusoh A. Rashid Idris, Ismail Oliva, Diego Noryanti, Muhammad Sait, Sadiq M. Al‑Utaibi, Khaled A. Amosa, Temitope Ibrahim Memon, Kamran Ali |
| author_facet | Bala, Abubakar Rahimi Zaman, Jusoh A. Rashid Idris, Ismail Oliva, Diego Noryanti, Muhammad Sait, Sadiq M. Al‑Utaibi, Khaled A. Amosa, Temitope Ibrahim Memon, Kamran Ali |
| author_sort | Bala, Abubakar |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data could be sent to powerful remote data centers for machine health analysis using artificial intelligence (AI) tools. However, centralized computing has its own challenges, such as privacy issues, long latency, and low availability. To overcome these problems, edge computing technology was embraced. Thus, instead of moving all the data to the remote server, the data can now transition on the edge layer where certain computations are done. Thus, access to the central server is infrequent. Although placing AI on edge devices aids in fast inference, it poses new research problems, as highlighted in this paper. Moreover, the paper discusses studies that use edge computing to develop artificial intelligence-based diagnostic and prognostic techniques for industrial machines. It highlights the locations of data preprocessing, model training, and deployment. After analysis of several works, trends of the field are outlined, and finally, future research directions are elaborated |
| first_indexed | 2025-11-15T03:54:18Z |
| format | Article |
| id | ump-44109 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:54:18Z |
| publishDate | 2024 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-441092025-04-23T06:04:40Z http://umpir.ump.edu.my/id/eprint/44109/ Artificial intelligence and edge computing for machine maintenance-review Bala, Abubakar Rahimi Zaman, Jusoh A. Rashid Idris, Ismail Oliva, Diego Noryanti, Muhammad Sait, Sadiq M. Al‑Utaibi, Khaled A. Amosa, Temitope Ibrahim Memon, Kamran Ali QA75 Electronic computers. Computer science QA76 Computer software Industrial internet of things (IIoT) has ushered us into a world where most machine parts are now embedded with sensors that collect data. This huge data reservoir has enhanced data-driven diagnostics and prognoses of machine health. With technologies like cloud or centralized computing, the data could be sent to powerful remote data centers for machine health analysis using artificial intelligence (AI) tools. However, centralized computing has its own challenges, such as privacy issues, long latency, and low availability. To overcome these problems, edge computing technology was embraced. Thus, instead of moving all the data to the remote server, the data can now transition on the edge layer where certain computations are done. Thus, access to the central server is infrequent. Although placing AI on edge devices aids in fast inference, it poses new research problems, as highlighted in this paper. Moreover, the paper discusses studies that use edge computing to develop artificial intelligence-based diagnostic and prognostic techniques for industrial machines. It highlights the locations of data preprocessing, model training, and deployment. After analysis of several works, trends of the field are outlined, and finally, future research directions are elaborated Springer Nature 2024-04 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/44109/1/Artificial%20intelligence%20and%20edge%20computing%20for%20machine%20maintenance-review.pdf Bala, Abubakar and Rahimi Zaman, Jusoh A. Rashid and Idris, Ismail and Oliva, Diego and Noryanti, Muhammad and Sait, Sadiq M. and Al‑Utaibi, Khaled A. and Amosa, Temitope Ibrahim and Memon, Kamran Ali (2024) Artificial intelligence and edge computing for machine maintenance-review. Artificial Intelligence Review, 57 (5). pp. 1-33. ISSN 0269-2821. (Published) https://doi.org/10.1007/s10462-024-10748-9 https://doi.org/10.1007/s10462-024-10748-9 |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Bala, Abubakar Rahimi Zaman, Jusoh A. Rashid Idris, Ismail Oliva, Diego Noryanti, Muhammad Sait, Sadiq M. Al‑Utaibi, Khaled A. Amosa, Temitope Ibrahim Memon, Kamran Ali Artificial intelligence and edge computing for machine maintenance-review |
| title | Artificial intelligence and edge computing for machine maintenance-review |
| title_full | Artificial intelligence and edge computing for machine maintenance-review |
| title_fullStr | Artificial intelligence and edge computing for machine maintenance-review |
| title_full_unstemmed | Artificial intelligence and edge computing for machine maintenance-review |
| title_short | Artificial intelligence and edge computing for machine maintenance-review |
| title_sort | artificial intelligence and edge computing for machine maintenance-review |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://umpir.ump.edu.my/id/eprint/44109/ http://umpir.ump.edu.my/id/eprint/44109/ http://umpir.ump.edu.my/id/eprint/44109/ http://umpir.ump.edu.my/id/eprint/44109/1/Artificial%20intelligence%20and%20edge%20computing%20for%20machine%20maintenance-review.pdf |