AI-Driven Reliability Analysis and Predictive Maintenance
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 31 May 2025 | Viewed by 868
Special Issue Editors
Interests: stochastic modelling for evaluation/prediction of performance indicators; reliability and maintenance modelling; AI-based maintenance optimization
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
AI-driven reliability analysis and predictive maintenance is a proactive and data-driven approach that leverages artificial intelligence (AI) technologies to analyse the reliability and proactively optimize maintenance of industrial systems and equipment. By harnessing the power of machine learning algorithms, predictive analytics, and advanced data processing techniques, this methodology allows us to efficiently anticipate and prevent equipment failures before they occur, thereby minimizing downtime, reducing maintenance costs, and maximizing operational efficiency.
This Special Issue will gather innovative research contributions and practical applications in the field of leveraging AI technologies for optimizing reliability analysis and predictive maintenance strategies in industrial settings.
Topics of interest for this Special Issue include, but are not limited to, the following:
- Advanced prognostics approaches;
- Advanced predictive maintenance models;
- Data-driven approaches for equipment failure prediction and prognosis;
- AI for predictive maintenance;
- Reinforcement learning for maintenance decision-making;
- Data mining and big data in prognostics and maintenance;
- Maintenance schedules and resource allocation using AI techniques;
- Integration of AI-driven solutions for reliability assessment and analysis;
- Challenges and opportunities involved in deploying AI-driven reliability analysis solutions in diverse industrial sectors.
Dr. Phuc Do
Prof. Dr. Cristiano Cavalcante
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- predictive maintenance
- data-driven prognosis
- AI-driven reliability analysis
- machine learning
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