Machinery Condition Monitoring and Intelligent Fault Diagnosis, 2nd Edition

Dear Colleagues,

Machinery condition monitoring and intelligent fault diagnosis have recently come to play a crucial role in automatic and intelligent industrial production processes. Based on machine learning, deep learning, and artificial intelligence, intelligent fault diagnosis was proposed, achieving remarkable improvements, especially in the face of unknown nonlinear machine behaviors and non-stationary data. However, there are still some problems in machinery condition monitoring and intelligent fault diagnosis that require further research, such as early fault detection features, multi-modal data fusion, and small-sample machine, multi-condition transfer, and interpretable deep learning algorithms.

To comprehensively report on the research progress in this field, disseminate excellent research results, and promote the development and application of machinery condition monitoring and intelligent fault diagnosis, this Special Issue presents advances in intelligent fault diagnosis algorithms, fault feature extraction, and intelligent machine monitoring.

This Special Issue includes, but is not limited to, the following topics:

• failure mechanism modeling for mechanical equipment; 
• monitoring signal processing for mechanical equipment; 
• intelligent feature extraction for condition monitoring;
• intelligent early fault detection and diagnosis;
• few-shot sample learning for fault detection;
• transfer learning-based methods for fault diagnosis;
• interpretable deep learning for fault diagnosis;
• hybrid models of data-driven and model-based approaches;
• sensor data fusion for fault diagnosis;
• measurement methods, technologies, and systems for fault diagnosis.

Prof. Dr. Hongli Gao
Dr. Zhichao You
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Machines is an international peer-reviewed open access monthly journal published by MDPI.

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• failure mechanisms modeling for mechanical equipment
• monitoring signal processing for mechanical equipment
• intelligent feature extraction for condition monitoring
• intelligent early fault detection and diagnosis
• few-shot sample learning for fault detection
• transfer-learning-based methods for fault diagnosis
• interpretable deep learning for fault diagnosis
• hybrid models of data-driven and model-based approaches sensor data fusion for fault diagnosis
• measurement methods, technologies, and systems for fault diagnosis

• Machinery Condition Monitoring and Intelligent Fault Diagnosis in Machines (5 articles)