Intelligent Fault Diagnosis of Rotating Machinery

Dear Colleagues,

Diverse rotating machineries, as crucial entities, are widely integrated in high-tech equipment of the Industry 4.0 era. Their performance is critical for the safety, precision, and availability of high-tech equipment. Timely diagnosing abnormal behavior and identifying fault locations are essential for in-service operation. To date, many new concepts, state-of-the-art deep learning tools, and smart IoT hardware have emerged and demonstrated great potential for intelligent fault diagnosis of rotating machinery.

This Special Issue will collect all research on intelligent fault diagnosis methods and applications in rotating machineries, including (but not limited to):

• Advanced sensing and perception;
• Advanced signal processing and deep feature mining;
• Knowledge discovery;
• Incipient anomaly detection;
• Deep-learning-assisted methods in fault diagnosis;
• Unbalanced dataset and mitigation methods;
• Transfer learning and domain adaption;
• Open-source datasets and dissemination;
• Adaptive and online learning;
• Physics-informed machine learning and hybrid methods;
• Digital-twin-based fault diagnosis;
• Failure prognosis;
• Hardware and IoT system for intelligent fault diagnosis;
• New applications of rotating machinery diagnosis.

Dr. Laifa Tao
Dr. Jie Liu
Dr. Shuai Zhao
Guest Editors

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