Intelligent Fault Diagnosis and Prognosis in Electric Motors and Energy Storage Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".
Deadline for manuscript submissions: 20 November 2025
Special Issue Editor
Interests: applied dynamics; artificial intelligence toward prognostics and health management (PHM); Robotic condition monitoring, diagnostic and prognostic systems; sensors and measurements; modeling and characterization of degradation phenomena; application of interests: complex energy systems including li-ion battery cells and wind energy conversion systems
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing adoption of electric motors and energy storage systems across industrial, transportation, and renewable energy sectors demands advanced techniques for ensuring their reliability and operational efficiency. This Special Issue aims to explore recent advancements in intelligent fault diagnosis and prognosis methods applied to these critical components. Emphasizing the integration of artificial intelligence (AI), machine learning (ML), signal processing, and digital twin technologies, the issue seeks to present cutting-edge research and practical applications that enable early fault detection, accurate fault classification, remaining useful life (RUL) estimation, and predictive maintenance strategies.
Topics of interest include, but are not limited to, the following:
- AI/ML-based fault detection and diagnosis techniques;
- Condition monitoring and data-driven health assessment;
- Prognostic modeling for electric motors and batteries;
- Hybrid approaches combining physics-based and data-driven methods;
- Edge and cloud computing applications in real-time monitoring;
- Sensor fusion and advanced signal processing for fault analysis;
- Case studies and industrial applications.
This Special Issue aims to bring together researchers, engineers, and practitioners from academia and industry to foster cross-disciplinary collaboration and accelerate innovation in the intelligent maintenance of electric machines and energy storage systems. We look forward to receiving your contributions.
Dr. Ki-Yong Oh
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.
Keywords
- fault diagnosis
- prognostics
- electric motors
- energy storage systems
- battery management systems
- condition monitoring
- predictive maintenance
- remaining useful life (RUL)
- machine learning
- artificial intelligence
- digital twin
- data-driven methods
- smart monitoring systems
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue policies can be found here.
