AI-Driven Intelligent Maintenance and Health Management for Complex Industrial Systems
A special issue of Machines (ISSN 2075-1702). This special issue belongs to the section "Machines Testing and Maintenance".
Deadline for manuscript submissions: 28 February 2026 | Viewed by 191
Special Issue Editors
Interests: intelligent prognostics and health management (PHM); anomaly detection; remaining useful life (RUL) prediction; sensor-based AI modeling; physics-informed machine learning for complex industrial systems (e.g., EVs, wind turbines, aerospace, energy storage systems)
Interests: intelligent fault diagnosis; railway transportation; industrial intelligence; prognosis and health management
Interests: industrial big data; intelligent maintenance and health management; uncertainty qualification
Special Issues, Collections and Topics in MDPI journals
Interests: UAV big data; low-altitude equipment; UAV intelligent operation and maintenance and digital twins
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The safe, efficient, and intelligent operation of complex industrial systems is essential to the sustainable development of key industries such as energy and power, rail transportation, aerospace, process manufacturing, and intelligent equipment. These systems often exhibit strong coupling, time-varying operating conditions, and diverse failure modes. Traditional scheduled or experience-based maintenance strategies are increasingly insufficient to meet modern demands for high reliability, reduced costs, and full life-cycle management.
Recent advances in sensing, data acquisition, and computing have accelerated the adoption of data-driven prognostics and health management (PHM) methods. In particular, integrating artificial intelligence (AI), edge computing, digital twins, and foundation models has unlocked new capabilities in early fault detection, remaining useful life (RUL) prediction, and adaptive maintenance optimization. Furthermore, the emergence of explainable AI techniques has enhanced the transparency and trustworthiness of intelligent maintenance systems.
This Special Issue will gather high-quality original research and reviews on the latest innovations, methodologies, and applications in AI-enabled PHM for complex industrial systems. Topics include, but are not limited to, the following:
- Multi-source heterogeneous data fusion;
- Anomaly detection, fault diagnosis, and RUL prediction;
- AI–digital twin integration for intelligent health management;
- Hybrid modeling combining physics-based and data-driven methods;
- Explainable AI and foundation models for industrial monitoring;
- Applications across energy, transportation, aerospace, and manufacturing.
Dr. Dandan Peng
Dr. Xiaoxi Hu
Dr. Jipu Li
Prof. Dr. Chuanjiang Li
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
- artificial intelligence
- diagnostics and prognostics
- digital twin
- explainable AI
- large foundation models
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