AI for Industrial Operation and Maintenance: Recognition Challenges with Limited Data Condition
A special issue of AI (ISSN 2673-2688).
Deadline for manuscript submissions: 31 July 2026 | Viewed by 56
Special Issue Editors
Interests: fault diagnosis; life prediction methods for electromechanical equipment; deep learning; deep neural networks
Interests: prognostics and health management; domain adaptation; machine learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in artificial intelligence (AI) have transformed industrial operation and maintenance (O&M), enabling predictive maintenance, fault diagnosis, and performance optimization. However, real-world industrial applications often face significant recognition challenges due to limited data availability—such as rare failure events, sparse labeled datasets, and noisy sensor measurements. These constraints hinder the deployment of robust AI models in critical industrial environments.
This Special Issue invites high-quality research addressing AI-driven recognition challenges in industrial O&M under data scarcity. Topics of interest include, but are not limited to, the following:
- Few-shot and zero-shot learning for fault diagnosis with minimal labeled examples.
- Data augmentation and synthetic data generation for industrial applications.
- Transfer learning and domain adaptation across different machines or plants.
- Hybrid physics-informed AI models combining data-driven and knowledge-based approaches.
- Self-supervised and semi-supervised learning leveraging unlabeled operational data.
- Uncertainty quantification and explainability in low-data regimes.
- Edge AI and federated learning for distributed industrial systems.
- Real-world case studies on AI deployment with limited training data.
We welcome original research articles, review papers, and industrial application studies that provide novel insights into overcoming data limitations in AI-based O&M. Submissions should demonstrate rigorous validation in real or simulated industrial environments.
This Special Issue aims to bridge the gap between academic research and industrial needs, fostering advancements in reliable and scalable AI solutions for Industry 4.0 and beyond.
We look forward to receiving your contributions.
Dr. Jialin Li
Dr. Yongchao Zhang
Dr. Chengying Zhao
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. AI 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 1600 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
- industrial artificial intelligence
- predictive maintenance
- few-shot learning
- fault diagnosis
- data-efficient learning
- transfer learning
- digital twin
- uncertainty quantification
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