Artificial Intelligence and Machine Learning in Maintenance: Exploring Reliability and Maintainability Perspectives
Maintenance is set of activities performed by service personnel to ensure availability of Machine to achieve the organisational objectives. The Reliability and Maintainability models have pivotal roles in Maintenance. A lot of up-gradation has happened in maintenance services since the industrial revolution. Therefore, it is direly required to seek out the latest trends, developments, limitations and to understand the adaptation of AI technologies in maintenance through the aspects of Reliability and Maintainability. For this purpose, an exhaustive literature review was carried out on the mentioned subject through an exploratory data analysis using MATLAB approach based on various publications reviewed from vast and relevant journals along with data available on open sources and Military Standards specific to maintenance sectors. The area of focus is Military Aviation Engineering and Management concerning ageing aircraft maintenance. So, this paper elicits the current trends of use of AI in maintenance models based on the research data of the past 09 years from 2016 to 2024. Literature classifications have not only been made as a part of scientific manner of review but also tailored based on parameters as per the guidance of military standards applicable to Reliability and Maintainability. Crucial observations have been made on recent researches and challenges like Cyber Security, covering all the latest developments including the numerous solutions approaches based on Analysis Models, Simulations, Data Science, AI etc and limitations. In this review, many new R and M models emerged and were highly suitable to be adapted in various sectors, especially aviation. In contrast, very few researches have been found in civil or military ageing aircraft maintenance. This paper is precious for beginners, researchers, and professionals currently working in maintenance management, system safety in civil or military organisations. They can refer to all the valuable references as provided herewith.
Authors
- Pradeep Kumar Arya
- Shaktiyavesh Nandan Pratap Singh (ORCID: https://orcid.org/0009-0008-2730-4596)
- Ajay Kumar (ORCID: https://orcid.org/0000-0002-8748-7717)
- Rohitashwa Shringi
Institutions
- Rajasthan Technical University (IN)
- Bennett University (IN)
Publication Details
- Published
- 2025-03-07
- DOI
- https://doi.org/10.1109/icdt63985.2025.10986353
- Citations
- 1
- Primary Topic
- Quality and Safety in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 3.53