Machine Health Monitoring and Fault Diagnosis Techniques

Dear Colleagues,

Machine health monitoring and fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial plants. Intelligent fault diagnosis has been proposed based on machine learning, deep learning, and artificial intelligence. However, there are still several issues that require further investigation—with intelligent fault diagnosis methodologies being among them, e.g.,  early fault detection features, the few-shot sample machine learning algorithm, data augmentation techniques for deep learning, data fusion method for domain adaptation, feature representation with self-supervision, and interpretable deep learning algorithms.

This Special Issue aims to highlight the state-of-the-art techniques used for machine health monitoring and fault diagnosis, especially for intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring. Topics include but are not limited to:

• Rotational machine monitoring and vibration signal processing
• Intelligent early fault detection and diagnosis
• Few-shot sample learning for fault detection
• Feature construction with intelligent algorithms
• Data-efficient domain adaptation and transfer learning
• Interpretable deep learning for fault diagnosis
• Data augmentation techniques for fault diagnosis
• Sensor data fusion for fault diagnosis
• Measurement methods, technologies, and systems for fault diagnosis

Dr. Shilong Sun
Prof. Dr. Changqing Shen
Dr. Dong Wang
Guest Editors

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