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Electronics
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Advanced Condition Monitoring and Fault Analysis in Industrial Electronics
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Advanced Condition Monitoring and Fault Analysis in Industrial Electronics

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Power Electronics".

Deadline for manuscript submissions: 15 November 2025 | Viewed by 3172

Special Issue Editors

Dr. Raimondas Pomarnacki

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University, 10105 Vilnius, Lithuania
Interests: signal processing; neural network; autonomous vehicle; motor control; electromagnetic devices; antenna; electrical engineering
Special Issues, Collections and Topics in MDPI journals
Dr. Janis Zakis

E-Mail Website
Guest Editor
Institute of Industrial Electronics and Electrical Engineering, Riga Technical University, 1048 Riga, Latvia
Interests: power electronics; energy conversion; renewable energy systems; distributed power generation; wireless power transfer
Special Issues, Collections and Topics in MDPI journals
Dr. Eldar Sabanovic

E-Mail Website
Guest Editor
Department of Electronic Systems, Vilnius Gediminas Technical University, 10105 Vilnius, Lithuania
Interests: software algorithms; embedded systems; deep neural network; signal processing; image processing; virtual sensing; intelligent transportation; autonomous vehicle system

Special Issue Information

Dear Colleagues,

Modern industrial electronic and electrical systems—such as converters, controllers, and electric motor drives—are prone to failures that can lead to production losses, service interruptions, and, in the worst cases, environmental disasters. These failures are typically caused by electrical, mechanical, or thermal stresses, either directly or indirectly. To mitigate these risks, regular maintenance and inspections following manufacturers' schedules are essential. In addition to hardware solutions, combining virtual sensors (VS) with advanced artificial intelligence (AI) tools presents a promising approach for addressing these issues.

Condition monitoring systems aim to minimize the risk of environmental disasters resulting from malfunctions in industrial electronic and electrical systems, including those used in wind turbines, electric vehicles, and industrial production facilities. The Virtual Sensor or Virtual Emulator concept, along with condition monitoring methodologies, enhances service availability and extends the operational life of equipment. In the renewable energy sector, these technologies could reduce reliance on fossil-fuel-based power generation, thus lowering the environmental impact of traditional fuels—an outcome critical for both the energy and offshore drilling industries. Moreover, condition monitoring of electrical devices, such as motor drives, encourages their use over hydraulic actuation systems, which pose significant environmental risks due to hydraulic oil leaks.

Importantly, by preventing failures in industrial electronic systems, this approach will also contribute to improved health, safety, and environmental (HSE) outcomes in the industry, reducing the risk of accidents and human injuries.
Optimizing industrial electronics—from electric vehicles to production chains—together with advances in semiconductor devices enables more energy-efficient operation across a wide range of speeds and power levels. However, this optimization also places additional stress on certain components under specific operating conditions, increasing their failure rates. For example, the use of frequency converters introduces extra electrical stress on the windings and bearings of electric machines. These factors have prompted the scientific community to study the effects of mechanical, thermal, and electrical stresses on the lifespan of energy system components and to develop models that predict early signs of failure. This field of research, known as diagnostics and prognostics, seeks to enable cost-effective maintenance, higher utilization of systems, and more efficient use of both material and human resources.

The integration of AI tools, IoT, and cloud computing in the development of monitoring solutions will bring significant benefits to the industry, enabling more efficient and reliable operation of industrial electronic systems.

Dr. Raimondas Pomarnacki
Dr. Janis Zakis
Dr. Eldar Sabanovic
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. Electronics 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

  • industrial electronics
  • electronic circuits
  • predictive control
  • failure analysis
  • digital twins
  • automation
  • Internet of Things
  • Internet of Vehicles

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Published Papers (4 papers)

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Research

29 pages, 4506 KB  
Article
Adaptive Deep Belief Networks and LightGBM-Based Hybrid Fault Diagnostics for SCADA-Managed PV Systems: A Real-World Case Study
by Karl Kull, Muhammad Amir Khan, Bilal Asad, Muhammad Usman Naseer, Ants Kallaste and Toomas Vaimann
Electronics 2025, 14(18), 3649; https://doi.org/10.3390/electronics14183649 - 15 Sep 2025
Viewed by 657
Abstract
Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light [...] Read more.
Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Fault Analysis in Industrial Electronics)
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25 pages, 6042 KB  
Article
An Improved LightGBM-Based Method for Series Arc Fault Detection
by Runan Song, Penghe Zhang, Yang Xue, Zhongqiang Wu and Jiaying Wang
Electronics 2025, 14(18), 3593; https://doi.org/10.3390/electronics14183593 - 10 Sep 2025
Viewed by 672
Abstract
As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved [...] Read more.
As low-voltage distribution networks incorporate increasingly diverse loads, series arc faults exhibit weak characteristics that are easily masked by load currents, leading to high misjudgment rates in traditional detection methods. This paper proposes a series arc fault detection method based on an improved Light Gradient Boosting Machine (LightGBM) model. First, a test platform containing 12 household loads was built to collect arc data from both individual and composite loads. Composite loads refer to composite load conditions where multiple devices are running simultaneously and arcing occurs on some loads. To address the challenge of feature extraction, Variational Mode Decomposition (VMD) is employed to isolate the fundamental frequency component. To enhance high-frequency arc characteristics, singular value decomposition (SVD) is then applied. A multidimensional statistical feature set—comprising peak-to-peak value, kurtosis, and other indicators—is constructed. Finally, the LightGBM algorithm is used to identify arc faults based on these features. To overcome the LightGBM model’s limited ability to focus on hard-to-classify samples, a dynamic weighted hybrid loss function is developed. Experiments demonstrate that the proposed method achieves 98.9% accuracy across 223,615 sample groups. When deployed on STM32H723VGT6 hardware, the average fault alarm time is 83.8 ms, meeting requirements. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Fault Analysis in Industrial Electronics)
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17 pages, 1587 KB  
Article
Accelerating Visual Anomaly Detection in Smart Manufacturing with RDMA-Enabled Data Infrastructure
by Yifan Wang, Tiancheng Yuan, Yuting Yang, Miao He, Richard Wu and Kenneth P. Birman
Electronics 2025, 14(12), 2427; https://doi.org/10.3390/electronics14122427 - 13 Jun 2025
Viewed by 899
Abstract
Industrial Artificial Intelligence (IAI) services are increasingly integral to smart manufacturing, especially in quality assurance tasks like defect detection. This paper presents the design, implementation, and evaluation of a video-based visual anomaly detection (VAD) system that runs at inspection stations on a smart [...] Read more.
Industrial Artificial Intelligence (IAI) services are increasingly integral to smart manufacturing, especially in quality assurance tasks like defect detection. This paper presents the design, implementation, and evaluation of a video-based visual anomaly detection (VAD) system that runs at inspection stations on a smart shop floor. Our system processes real-time video streams from multiple cameras mounted around a conveyor belt to detect surface-level defects in mechanical components. To meet stringent latency and accuracy requirements, an edge-cloud architecture powered by AI accelerators and InfiniBand networking is adopted. The IAI service features key frame extraction modules, fine-tuned lightweight VAD models, and optimization techniques such as batching and microservice-level parallelism. The design choices of AI modules are carefully evaluated to balance effectiveness and efficiency. As a result, the system latency is optimized by 57%. In addition to the high-performance solution, a cost-efficient alternative is also suggested that is able to complete the task within the time frame. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Fault Analysis in Industrial Electronics)
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24 pages, 12092 KB  
Article
Time and Frequency Domain Blind Deconvolution Based on Generalized Lp/Lq Norm for Rolling Bearing Fault Diagnosis
by Baohua Wang, Zhaoliang Li, Jiacheng Zhang and Weilong Wang
Electronics 2025, 14(11), 2243; https://doi.org/10.3390/electronics14112243 - 30 May 2025
Viewed by 583
Abstract
In rolling bearing fault diagnosis, faint fault features are often obscured by ambient noise, limiting the feature extraction capabilities of traditional methods. To address this problem, a time and frequency domain blind deconvolution method based on the generalized Lp/Lq [...] Read more.
In rolling bearing fault diagnosis, faint fault features are often obscured by ambient noise, limiting the feature extraction capabilities of traditional methods. To address this problem, a time and frequency domain blind deconvolution method based on the generalized Lp/Lq norm (G-Lp/Lq-TF) is proposed. Through an analysis of the generalized Lp/Lq norm’s properties, two monotonic yet opposing sparsity-related value intervals are identified and applied separately in the time and frequency domains. The optimal selection range for p and q values is then determined. A hybrid optimization criterion is designed to enforce mutual constraints between the two intervals, ensuring an optimal solution. A convolutional neural network is utilized to serve as the blind deconvolution filter, with backpropagation-based automatic differentiation used for gradient-based optimization of filter coefficients. This approach provides adequate decision-making guidance for selecting p and q values, which was lacking in previous studies on the sparsity of the generalized Lp/Lq norm. It also mitigates noise-spike sensitivity and frequency component loss when applied independently in either domain. Validation using simulated signals and three real-world bearing fault datasets confirms that the proposed method outperforms existing methods in both fault feature extraction and stability. Full article
(This article belongs to the Special Issue Advanced Condition Monitoring and Fault Analysis in Industrial Electronics)
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