Machinery Condition Monitoring and Intelligent Fault Diagnosis

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 has been proposed and achieved remarkable improvements, especially in the face of unknown nonlinear machine behavior and non-stationary data. However, there are still some machinery condition monitoring and intelligent fault diagnosis problems that require further research, such as early fault detection features, a small sample machine learning algorithm, multi-condition transfer learning algorithm, multi-modal data fusion method, and interpretable deep learning algorithm.

To comprehensively report 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 focuses on presenting intelligent fault diagnosis algorithm development, fault feature extraction, and intelligent machine monitoring.

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

• 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.

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.