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Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment
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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: 30 September 2026 | Viewed by 3077

Special Issue Editors

Dr. Cen Chen

E-Mail Website
Guest Editor
School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin 150001, China
Interests: reliability prediction; fault diagnosis and health management of electronic system
Prof. Dr. Erping Deng

E-Mail Website
Guest Editor
School of Electrical and Automation Engineering, Hefei University of Technology, Hefei 230009, China
Interests: power semiconductor device packaging; testing; reliability and failure analysis
Dr. Dawei Liang

E-Mail Website1 Website2
Guest Editor
Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield S1 3JD, UK
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 250 words) can be sent to the Editorial Office for assessment.

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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Published Papers (6 papers)

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Research

23 pages, 4929 KB  
Article
Research on the Coordination of Surge Protectors in Communication Power Systems
by Kang Yang, Hongyan Xing, Zhoulong Wang and Linlong Shi
Energies 2026, 19(10), 2454; https://doi.org/10.3390/en19102454 - 20 May 2026
Viewed by 140
Abstract
To address the issue of coordination failure in multi-stage surge protective devices (SPDs) under lightning surges in communication power systems, this study employs traveling wave propagation theory and electromagnetic transient simulations using the PSCAD/EMTDC platform. It systematically evaluates how lightning strike location, interstage [...] Read more.
To address the issue of coordination failure in multi-stage surge protective devices (SPDs) under lightning surges in communication power systems, this study employs traveling wave propagation theory and electromagnetic transient simulations using the PSCAD/EMTDC platform. It systematically evaluates how lightning strike location, interstage cable length, and load type affect energy coordination and overvoltage response in a two-stage SPD configuration. By combining time-domain and frequency-domain analysis, the coupling mechanism of SPD conduction timing is revealed. There exists a critical length for the interstage cable to ensure coordinated operation of the SPDs. This critical length decreases with increasing surge intensity but increases significantly with greater lightning strike distance. Incorporating an appropriate series inductor can provide the necessary time delay, serving as an alternative to using a long cable. For capacitive loads, although an excessively short cable can reduce the amplitude of oscillatory voltage spikes, it aggravates the surge steepness, thereby stressing the SPD. These oscillations can be effectively suppressed by installing a damping resistor in front of the SPD2. Furthermore, the study reveals a strong coupling between energy coordination and overvoltage behavior under capacitive load conditions, indicating that the two must be jointly optimized. The parameter configurations and practical recommendations presented offer quantitative design guidance for SPD selection, cable layout, and resonance suppression in communication power systems. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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20 pages, 861 KB  
Article
Fault Diagnosis for Active Distribution Network Based on Colored and Fuzzy Colored Petri Net
by Yulong Qin, Yifan Hou, Han Zhang and Ding Liu
Energies 2026, 19(9), 2162; https://doi.org/10.3390/en19092162 - 30 Apr 2026
Viewed by 307
Abstract
Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the [...] Read more.
Accurate and rapid fault diagnosis is critical for active distribution networks characterized by growing structural complexity and diverse load profiles. This paper proposes a two-stage fault diagnosis framework that synergistically combines colored Petri nets (CPN) and fuzzy colored Petri nets (FCPN). In the first stage, a CPN fault zone search model employing a breadth-first search (BFS) strategy is developed to identify suspected faulty components by processing circuit breaker operation information and grid topology. In the second stage, an FCPN diagnosis model is constructed by extending hierarchical fuzzy Petri nets through color assignment to confidence tokens. A key feature of this model is a dedicated initial confidence assessment module that dynamically evaluates the reliability of protection and circuit breaker actions by synthesizing device self-check alarms and operational timing information, thereby overcoming the limitation of empirical, static confidence assignment in existing methods. The resulting initial confidence values are then propagated through a hierarchical confidence inference module to determine the fault likelihood of each suspected component. Comparative simulations across four fault scenarios demonstrate that the proposed method achieves higher diagnostic accuracy and stronger fault tolerance than state-of-the-art approaches, correctly identifying all faulty components even under degraded alarm conditions. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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16 pages, 1968 KB  
Article
Aging Evaluation Method of Oil-Paper Insulation Based on Raman Spectrum and Frequency-Domain Spectroscopy
by Zhuang Yang, Zhixian Yin, Fan Zhang, Qiuhong Wang and Changding Wang
Energies 2026, 19(9), 2139; https://doi.org/10.3390/en19092139 - 29 Apr 2026
Viewed by 256
Abstract
In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, [...] Read more.
In order to achieve more accurate and efficient oil-paper insulation aging assessment, and to improve the operation and maintenance level of oil-paper insulated power equipment, this paper proposes an aging evaluation method of oil-paper insulation based on Raman spectrum and frequency-domain spectroscopy. First, oil-paper insulation samples with different aging degrees were prepared by an accelerated thermal aging test in this experiment. Then, Raman spectroscopy and frequency-domain dielectric spectroscopy were used to examine the samples and analyze the aging characteristics of the samples by LightGBM R2019b. Finally, the gray neural network is used to establish a prediction model for the degree of polymerization of insulating paper based on frequency-domain dielectric features and Raman spectral features. The results of this study showed that there is a certain correlation between the Raman characteristics of insulating oil and the FDS characteristics of insulating paper. The average absolute error of the prediction of the R-F-PGNN model developed in this paper is 20.4. The research in this paper provides a strong support for the development of Raman spectroscopy diagnosis technology for oil-paper insulation aging in the power industry, which has certain academic value and engineering application significance. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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23 pages, 8136 KB  
Article
Fault Prediction Method of Boost Converter Based on Multi-Modal Components and Temporal Convolutional Networks
by Jiaying Li, Chengye Zhu, Yuhang Dong and Min Xia
Energies 2026, 19(8), 1974; https://doi.org/10.3390/en19081974 - 19 Apr 2026
Viewed by 230
Abstract
During long-term operation, power electronic converters are jointly affected by component degradation and operational disturbances, leading to pronounced nonstationary and multi-scale characteristics in output-voltage signals, which pose challenges for fault prediction. To address the degradation forecasting problem of Boost converter output voltage, this [...] Read more.
During long-term operation, power electronic converters are jointly affected by component degradation and operational disturbances, leading to pronounced nonstationary and multi-scale characteristics in output-voltage signals, which pose challenges for fault prediction. To address the degradation forecasting problem of Boost converter output voltage, this paper proposes a multi-scale temporal modeling method that integrates multivariate variational mode decomposition, distribution entropy-based complexity features, and a temporal convolutional network. Multivariate variational mode decomposition is employed to achieve frequency-aligned decomposition of the voltage signal, enabling effective separation of dynamic components at different scales. Distribution entropy is then introduced to characterize the evolution of local structural complexity in each mode, and multi-channel complexity feature sequences are constructed accordingly. Based on these features, a temporal convolutional network is used to perform unified modeling of short-term fluctuations and long-term degradation trends. Experimental results demonstrate that the proposed approach achieves consistently high accuracy across multiple independent runs, with average RMSE ranging from 0.0111 to 0.0179 and average MAPE from 1.15% to 1.84%. The low standard deviations further confirm its robustness for degradation trend prediction under varying operating conditions. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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23 pages, 4464 KB  
Article
Diagnosis of Cascaded Open/Short-Circuit Fault in Three-Phase Inverter Using Two-Stage Interval Sliding Mode Observer
by Cen Chen, He Du, Xuerong Ye, Xiaowen Nie, Chunqing Wang and Guofu Zhai
Energies 2025, 18(24), 6498; https://doi.org/10.3390/en18246498 - 11 Dec 2025
Viewed by 609
Abstract
A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for [...] Read more.
A three-phase inverter faces the risk of open-circuit (OC) and short-circuit (SC) faults in operation and requires real-time fault diagnosis. However, existing diagnosis methods have the following limitations: (1) insufficient rapid diagnosis capability for multi-switch cascaded faults; (2) inability to achieve diagnosis for hybrid OC and SC faults. To address these issues, this paper proposes a diagnosis method for cascaded switch open/short-circuit fault in a three-phase inverter based on a two-stage interval sliding mode observer (ISMO). First, by establishing a mixed logic dynamic (MLD) model considering open- and short-circuit faults, the different fault operating states of the three-phase inverter can be fully characterized. Furthermore, a two-stage cascaded ISMO was designed. The pre-stage ISMO rapidly detects abnormal status and fault phase, while the post-stage ISMO accurately isolates OC and SC faults. After diagnosis, the corresponding fault identification of the observer is set for the next fault diagnosis, achieving the sequential diagnosis of cascaded faults. The proposed diagnosis method was tested to validate its effectiveness. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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16 pages, 1587 KB  
Article
Prognostic Modeling of Thermal Runaway Risk in Lithium-Ion Power Batteries Based on Multivariate Degradation Data
by Yigang Lin, Shihao Guo, Mei Ye, Weifei Qian, Huiyu Chen, Qiuying Chen and Ziran Wu
Energies 2025, 18(23), 6241; https://doi.org/10.3390/en18236241 - 27 Nov 2025
Cited by 1 | Viewed by 1033
Abstract
Lithium-ion batteries serve as critical energy storage units for electric vehicles, unmanned aerial vehicles, and other emerging transportation systems. Numerous real-world incidents have demonstrated that thermal runaway (TR) remains a predominant cause of spontaneous combustion in these applications. Concerns over TR risks have [...] Read more.
Lithium-ion batteries serve as critical energy storage units for electric vehicles, unmanned aerial vehicles, and other emerging transportation systems. Numerous real-world incidents have demonstrated that thermal runaway (TR) remains a predominant cause of spontaneous combustion in these applications. Concerns over TR risks have significantly hindered broader adoption of lithium-ion batteries. While existing research predominantly focuses on battery heat generation mechanisms, TR initiation processes, and advanced materials with enhanced safety, limited attention has been paid to TR risk evolution induced by cycle-induced performance degradation. To address this gap, this study proposes a data-driven prognostic framework for quantifying TR risks under battery aging scenarios. Leveraging the Open Access XJTU Battery Dataset, we first identify eight degradation-sensitive parameters (including mean current, current standard deviation, and charging time, etc.) by analyzing temporal degradation patterns within characteristic segments of charging curves. These parameters are then fused into a composite degradation index through Physics-Informed Neural Networks (PINNs). Recognizing the stochastic nature of both degradation trajectories and TR-triggering stresses, a Wiener process-based random failure threshold model is developed to probabilistically predict TR risks under time-varying operational conditions. The proposed methodology enables quantitative risk assessment throughout battery service life, offering a novel perspective for aging-aware battery safety management. Full article
(This article belongs to the Special Issue Advances in Condition Monitoring and Fault Diagnosis of Electrical Equipment)
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