Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".
Deadline for manuscript submissions: 15 September 2025 | Viewed by 53
Special Issue Editors
Interests: reliability prediction; fault diagnosis and health management of electronic system
Interests: power semiconductor device packaging; testing; reliability and failure analysis
Interests: multi-physical design of electrical machines; parameter estimation of permanent magnet machines
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Modern industrial and energy systems rely heavily on the uninterrupted operation of electrical equipment, such as power switches, transformers, generators, electric motors and their corresponding drive systems. With the increasing complexity of these systems and their widespread applications across diverse domains—such as renewable energy, smart grids, electric vehicles, and industrial automation—ensuring their reliability and efficiency has become a critical priority. Condition monitoring and fault diagnosis technologies for electrical equipment can significantly reduce downtime, lower operational costs, and prevent catastrophic failures. While recent advancements in sensor technologies, data analytics, artificial intelligence, and digital twins have markedly enhanced early anomaly detection, fault diagnosis, and prognosis capabilities, challenges persist in adaption to complex operating conditions; balancing strategy, accuracy, and cost; and integrating solutions into practical systems.
This Special Issue, “Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment”, aims to showcase innovative research and methodologies addressing these challenges. We cordially invite submissions focusing on novel condition-monitoring technologies, advanced diagnostic algorithms, intelligent fault prediction, and smart maintenance strategies. Contributions may encompass theoretical developments and practical applications, with an emphasis on the scalability, robustness, and applicability of solutions in electrical equipment in the industrial and energy sectors.
Topics of interest include, but are not limited to, the following:
- Real-time multi-physical (electromagnetic/thermal/vibration/acoustic) condition monitoring;
- AI, machine learning, and deep learning approaches for health indicator mining;
- Digital twin-based predictive maintenance frameworks;
- Advanced model-based fault-tolerant control and design;
- Integration of AI and edge computing into predictive maintenance frameworks.
Dr. Cen Chen
Prof. Dr. Erping Deng
Dr. Dawei Liang
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. Energies 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 2600 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
- condition monitoring
- fault diagnosis
- predictive maintenance
- artificial intelligence (AI)
- electrical equipment
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