Machine Health Monitoring And Fault Diagnosis Techniques (volume Ii)

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 of interest include, but are not limited to: machine learning, deep learning, artificial intelligence, early fault detection features, few-shot sample machine learning algorithm, data augmentation techniques for deep learning, data fusion methods for domain adaptation, feature representation with self-supervision, and interpretable deep learning algorithms. Manuscripts should be submitted online at www.mdpi.com and will be peer-reviewed.