Explainable and Trustworthy Artificial Intelligence for Intelligent Inspection, Health Monitoring, and Prognostics of Complex Engineering Systems

Dear Colleagues,

In the era of Industry 5.0, complex engineering systems—including transportation equipment, energy and mining machinery, industrial manufacturing systems, and civil infrastructure such as bridges and roads—are becoming increasingly intelligent, interconnected, and safety-critical. Their reliable operation, structural integrity, and lifecycle efficiency are closely related to public safety, economic stability, and industrial competitiveness. Therefore, intelligent technologies for inspection, health monitoring, prognostics, and maintenance decision-making must not only be accurate, but also explainable, trustworthy, and practically reliable.

As mechanical, structural, electronic, and cyber–physical systems become more complex, conventional black-box models often lack interpretability, physical consistency, uncertainty awareness, and actionable insight. Recent advances in explainable artificial intelligence (XAI), trustworthy machine learning, physics-informed modeling, knowledge-guided learning, digital twins, and human-in-the-loop decision-making provide new opportunities to enhance the reliability and credibility of intelligent engineering systems and facilitate their practical deployment in real-world engineering applications. This Special Issue aims to promote AI methods in which explainability and trustworthiness are central scientific contributions rather than supplementary visualization tools or superficial post hoc add-ons.

We welcome original research articles and comprehensive reviews, particularly contributions in which explainability, trustworthiness, uncertainty awareness, physical consistency, or accountable decision-making constitutes a core methodological or application-driven contribution. Topics of interest include, but are not limited to:

• Explainable machine learning for inspection, fault detection, health assessment, and anomaly localization;
• Physics-informed and knowledge-guided AI for transparent diagnosis and prognostics;
• Trustworthy remaining useful life prediction, uncertainty-aware reliability assessment, and explainable predictive maintenance;
• Interpretable degradation modeling and uncertainty-aware prognostics;
• Explainable multimodal sensing, data fusion, and cross-domain transfer learning;
• Digital twin-enabled monitoring with transparent, verifiable, and trustworthy decision support;
• Human-in-the-loop AI for accountable diagnosis, operation, and maintenance;
• Evaluation, validation, and benchmarking of explanations for anomaly localization and defect identification;
• Trustworthy and explainable AI applications in transportation, energy, mining, manufacturing, civil infrastructure, and structural health monitoring.

Dr. Sixiang Jia
Dr. Xingxing Jiang
Dr. Jinrui Wang
Dr. Khandaker Noman
Guest Editors

Manuscript Submission Information

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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-anonymized 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.

Publisher’s Notice

Following discussions between the Machines Editorial Office and the Guest Editor team, the Special Issue description has been revised. This change has been approved by the journal Editorial Board, and the Special Issue website has been updated accordingly on 16 July 2026. The Special Issue will continue to be handled by the Guest Editor team in accordance with MDPI’s Special Issue and editorial policies.