Recent Advances in Condition Monitoring and Fault Diagnosis

Dear Colleagues,

Condition monitoring and fault diagnosis are essential for ensuring the safety, reliability, and efficiency of modern industrial systems. With the rapid development of sensing technologies, signal processing methods, and intelligent algorithms, data-driven and physics-informed approaches have significantly improved the capability of detecting, diagnosing, and predicting equipment faults under complex operating conditions.

This Special Issue aims to highlight recent advances in theories, methodologies, and practical applications for condition monitoring, anomaly detection, fault diagnosis, and prognostics (e.g., remaining useful life estimation). We welcome innovative research that integrates advanced sensing, efficient algorithms, and real-world validation across diverse industrial scenarios. Contributions that demonstrate robustness, interpretability, and deployability in practical environments are particularly encouraged.

Topics of Interest (non-exhaustive):

• Signal processing and feature extraction for monitoring data;
• Machine learning and deep learning for fault detection and diagnosis;
• Few-shot, one-shot, zero-shot transfer, and self-supervised learning under limited labels;
• Domain adaptation and robustness to noise, drift, and missing data;
• Physics-informed and hybrid modeling approaches;
• Prognostics and health management, including RUL prediction;
• Multisensor fusion and distributed monitoring systems;
• Explainable, reliable, and uncertainty-aware diagnosis methods;
• Benchmarking, datasets, and real-world industrial case studies.

Dr. Cong Peng
Dr. Zisheng Wang
Guest Editors

Manuscript Submission Information

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