Dear Colleagues,

Machinery and electrical systems require reliable and risk-free decision making for control, testing, diagnosis, prognosis, and maintenance actions. These capabilities form the foundation of critical fields such as energy, transportation, information and communication technology, manufacturing, industrial machinery, electrical systems, logistics, and more. These fields are considered essential in high-tech industries and plants. Inevitably, these systems experience health degradation or failure, which significantly impacts their operational performance and structural integrity. Therefore, diagnosis plays a crucial role in identifying electrical or mechanical failures and ensuring the reliability of these systems. Diagnostic or prognostic assessments usually lead to decision-making processes such as adjusting set points, halting operations, performing maintenance actions, or reconfiguring controls. 

This Special Issue seeks contributions on the subject of fault diagnosis in various domains, including electric machines; power electronics; power generation systems; power transmission systems; integrated energy systems; chemical mechanical systems; electric vehicles/drones/planes/helicopters; and heating, ventilating, and air conditioning systems. The contributions should be based on experimental research and simulation studies. We particularly appreciate works that highlight new diagnostic methods, advanced signal processing methods, novel analysis approaches, innovative diagnostic topologies or instruments, artificial intelligence, deep learning, predictive maintenance, and any emerging fault-tolerant control strategies. Prospective authors are invited to submit original contributions to this Special Issue, which will cover a broad range of topics, including, but not limited to, the following:

1. Fault detection, diagnosis, and prognosis in machinery and electrical systems;
2. Advanced modeling method for fault diagnosis;
3. Condition monitoring and maintenance of machinery and electrical systems;
4. Reliability and maintainability engineering of fault-tolerant control for machinery and electrical systems;
5. Artificial intelligence and deep learning in fault diagnosis;
6. Advanced measurement and signal processing technologies;
7. Control techniques for cyber-physical systems;
8. Fault-tolerant control in electric machines; 
9. Fault diagnosis in power electronics;
10. Fault diagnosis and control in integrated energy systems.

Prof. Dr. Adrian David Cheok
Guest Editor

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. Applied Sciences is an international peer-reviewed open access semimonthly 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.

• fault detection, diagnosis, and prognosis in machinery and electrical systems
• advanced modeling method for fault diagnosis
• condition monitoring and maintenance of machinery and electrical systems
• reliability and maintainability engineering of fault-tolerant control for machinery and electrical systems
• artificial intelligence and deep learning in fault diagnosis
• advanced measurement and signal processing technologies
• control techniques for cyber-physical systems
• fault-tolerant control in electric machines
• fault diagnosis in power electronics
• fault diagnosis and control in integrated energy systems