Artificial Intelligence for Diagnosis, Detection, Monitoring and Maintenance

Dear Colleagues,

Artificial intelligence is transforming how machines are diagnosed, monitored, and maintained across sectors, from manufacturing and energy to transportation and civil infrastructure. Building on decades of model‑based diagnostics and condition monitoring, recent advances in deep learning, multimodal sensing, and edge computing now enable robust, real‑time decisions to be made when there is still uncertainty. This Special Issue aims to collate cutting‑edge research that bridges algorithms and deployment, emphasizing reliability, data efficiency, and interpretability.

We welcome contributions that focus on the following topics: (i) foundations and benchmarks for fault diagnosis, anomaly/defect detection, prognostics, and health management; (ii) infrastructure status monitoring using multimedia data (vision, audio, vibration, thermal, radar, and fused modalities); (iii) self‑supervised, few/zero‑shot, and federated learning for scarce or private data; (iv) trustworthy AI—uncertainty quantification, explainability, robustness, and safety; (v) edge/cloud architectures, digital twins, and physics‑informed learning for scalable maintenance; and (vi) lifecycle considerations, including dataset shift, continual learning, and standards.

We solicit the submission of rigorous original research, comprehensive surveys, reproducible benchmarks, and impactful application studies. Submissions should clearly articulate problem definitions, validated methodology, and practical implications, preferably with open datasets or code. Case studies in fault diagnosis, defect/anomaly detection, predictive maintenance, and infrastructure monitoring using multimodal/multimedia data are especially encouraged.

Dr. Xuefeng Ni
Dr. Kechen Song
Dr. Yunkang Cao
Guest Editors

Manuscript Submission Information

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