Introduction and Aim & Scope

Maintaining complex systems requires reliability assessment, prognostics and maintenance planning to be considered together. Artificial intelligence (AI) can combine condition-monitoring data, maintenance records, and engineering knowledge to detect faults, estimate reliability and remaining useful life, and support maintenance decisions. However, effective interventions must account for uncertainty, component dependencies, operational constraints, safety, and shared resources. This special issue invites theoretical, experimental, and industrial contributions connecting AI-based predictions with practical maintenance actions, including energy, manufacturing, transport, and other interconnected complex systems. Contributions should link component-level observations to system performance, clarify methodological and data limitations, and discuss implications for reliability, availability, safety, cost, or resource use.

This special issue aims to collect articles that have theoretical studies and applications on AI-driven reliability, prognostics and maintenance of complex systems. The scope of this special issue covers, but is not limited to, the following research areas:

Topic of Interest

• AI in advanced simulation techniques for risk and reliability analysis
• AI in availability and safety
• AI in condition-based maintenance models
• AI in data-driven modeling-based fault prognostics
• AI in data-driven reliability analysis
• AI in dependability techniques and methodologies for complex systems
• AI in design for maintainability
• AI in design for reliability
• AI in design for resilience and robustness
• AI in maintainability analysis, maintenance modeling and planning
• AI in maintenance decisions under uncertainty
• AI in maintenance management
• AI in maintenance strategy optimization approaches
• AI in model-based system reliability assessment
• AI in modeling and simulation of complex systems
• AI in predictive maintenance frameworks
• AI in reliability management
• AI in reliability of machine learning-based condition monitoring systems
• AI in reliability-centered maintenance
• AI in system reliability and reliability modelling
• AI in uncertainty and sensitivity analysis

Important Dates
Manuscript submission opening: November 1, 2026
Last date for submitting the manuscript: May 31, 2027


Manuscript Submission Information

Articles have to be prepared carefully according to the guide For Authors at the journal website https://ijmems.in/forauthors.php, and to be submitted through online IJMEMS Submission System at https://submission.ijmems.in . On the first page of the manuscript, before the title of the article, kindly write as “AI-Driven Reliability, Prognostics and Maintenance of Complex Systems”.

Authors should note that all articles submitted should be original and should not have been submitted anywhere else for consideration for publication. All articles will be reviewed in double blind review process as per the journal policy. More information can be found at the journal website at https://ijmems.in Or https://ijmems.in/ethicalissues.php

Special Note: There will be NO APC for the articles accepted in this Special Issue. The corresponding author must submit the articles with his/her institutional email id.

Guest Editors

Prof. (Dr.) Liudong Xing
Department of Electrical and Computer Engineering,
University of Massachusetts, Dartmouth, USA.

Prof. (Dr.) Haidong Shao
College of Mechanical and Vehicle Engineering,
Hunan University, Changsha, China.

Dr. Xiaoge Zhang
Department of Industrial and Systems Engineering,
The Hong Kong Polytechnic University,
Hung Hom, Kowloon, Hong Kong.

Prof. (Dr.) Mangey Ram
Department of Mathematics, Computer Science and Engineering,
Graphic Era Deemed to be University, Dehradun, India.