Intelligent Machinery Fault Diagnosis and Maintenance

Dear Colleagues,

The needs and benefits of intelligent machinery fault diagnosis and maintenance are apparent, including enhanced reliability and safety of complex engineering systems, and reduced operational and support costs, among others. With the advent of deep learning algorithms featuring intricate layers, significant efforts have been made in intelligent fault diagnosis and maintenance for machinery systems. However, some challenges still need to be addressed, such as the limitation in fault data availability, the desire for real-time fault diagnosis, the interpretability of fault diagnosis, system-level fault diagnosis, and optimal maintenance strategies. This Special Issue focuses on collecting current research work that advances or improves the field of intelligent machinery fault diagnosis and maintenance.

This Special Issue aims to feature high-quality research papers in the realm of intelligent machinery fault diagnosis and maintenance. Therefore, we invite researchers, engineers, and experts to contribute the latest research work on intelligent machinery fault diagnosis and maintenance. These studies are expected to provide novel theories and algorithms, analytical models, and experiments that contribute to the field.  

In this Special Issue, original research papers and reviews are welcome. Research topics may include, but are not limited to, the following topics:

• Novel machine learning algorithms for intelligent fault diagnosis;
• Novel methods to identify anomalies within large data sets;
• Advanced machine learning methods integrated with domain knowledge;
• Novel intelligent fault diagnosis with interpretable models;
• Digital twin technologies for intelligent fault diagnosis;
• Novel transfer learning approaches for intelligent fault diagnosis;
• Methods for determining maintenance strategies based on machinery conditions;
• Optimization methods for scheduling maintenance;

We look forward to receiving your contributions.

Dr. Meng Ma
Dr. Haidong Shao
Dr. Xu Guo
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.

• intelligent fault diagnosis
• optimization of maintenance
• machine learning
• feature extraction
• complex engineering systems