Explainable Artificial Intelligence for Fault Diagnosis, Remaining Useful Life Prediction and Prognosis of Transportation Equipment

Dear Colleagues,

In the era of Industry 5.0, transportation equipment—such as high-speed trains, aircraft, ships, and intelligent vehicles—has become the backbone of modern industrial and social infrastructure. As these systems grow to become more intelligent, integrated, and safety-critical, ensuring their reliable operation has become a top priority. The health, safety, and efficiency of such equipment are closely tied to national economic security, public safety, and industrial competitiveness. Therefore, developing intelligent, interpretable, and trustworthy operation and maintenance (O&M) technologies for transportation equipment is of paramount importance.

With the growing complexity of mechanical, electronic, and cyber-physical components in transportation systems, traditional black-box diagnostic models often fall short in providing transparency and actionable insights. Inspired by recent advances in explainable artificial intelligence (XAI), causality learning, and physics-informed modeling, this Special Issue focuses on the emerging frontier of interpretable intelligent O&M for transportation equipment. The aim is to promote trustworthy and transparent methods for health monitoring, fault diagnosis, prognostics, and maintenance decision-making, thereby supporting safe and efficient operation of critical transportation systems in the Industry 5.0 era.

We encourage researchers to submit original research articles and comprehensive reviews in this domain. Potential areas of interest include, but are not limited to the following:

• Interpretable machine learning methods for fault diagnosis and health assessment of transportation equipment;
• Causality-driven modeling and reasoning in intelligent maintenance systems;
• Knowledge-infused and physics-informed approaches for interpretable prognosis;
• Transparent decision-making systems for maintenance optimization in transportation platforms;
• Multimodal and cross-domain data fusion with interpretability guarantees;
• Human-in-the-loop frameworks for collaborative maintenance and decision support;
• Visualization and explanation tools for fault localization and diagnostic reasoning;
• Real-world case studies in rail, aerospace, maritime, or intelligent vehicle maintenance.

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

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