Acácio M. R. Amaral^1,2,*

CMC-Computers, Materials & Continua, Vol.85, No.2, pp. 3825-3859, 2025, DOI:10.32604/cmc.2025.067179 - 23 September 2025

Abstract Power converters are essential components in modern life, being widely used in industry, automation, transportation, and household appliances. In many critical applications, their failure can lead not only to financial losses due to operational downtime but also to serious risks to human safety. The capacitors forming the output filter, typically aluminum electrolytic capacitors (AECs), are among the most critical and susceptible components in power converters. The electrolyte in AECs often evaporates over time, causing the internal resistance to rise and the capacitance to drop, ultimately leading to component failure. Detecting this fault requires measuring the… More >

Zuhaib Nishtar^1,*, Fangzong Wang¹, Fawwad Hassan Jaskani², Hussain Afzaal³

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.3, pp. 2919-2956, 2025, DOI:10.32604/cmes.2025.065098 - 30 June 2025

Abstract This research aims to address the challenges of fault detection and isolation (FDI) in digital grids, focusing on improving the reliability and stability of power systems. Traditional fault detection techniques, such as rule-based fuzzy systems and conventional FDI methods, often struggle with the dynamic nature of modern grids, resulting in delays and inaccuracies in fault classification. To overcome these limitations, this study introduces a Hybrid Neuro-Fuzzy Fault Detection Model that combines the adaptive learning capabilities of neural networks with the reasoning strength of fuzzy logic. The model’s performance was evaluated through extensive simulations on the… More >

Qiang Ma^1,2,3,4,5, Zhuopei Wei^1,2, Kai Yang^1,2,*, Long Tian^1,2, Zepeng Li^1,2

Structural Durability & Health Monitoring, Vol.19, No.4, pp. 1011-1035, 2025, DOI:10.32604/sdhm.2025.060596 - 30 June 2025

Abstract An intelligent diagnosis method based on self-adaptive Wasserstein dual generative adversarial networks and feature fusion is proposed due to problems such as insufficient sample size and incomplete fault feature extraction, which are commonly faced by rolling bearings and lead to low diagnostic accuracy. Initially, dual models of the Wasserstein deep convolutional generative adversarial network incorporating gradient penalty (1D-2DWDCGAN) are constructed to augment the original dataset. A self-adaptive loss threshold control training strategy is introduced, and establishing a self-adaptive balancing mechanism for stable model training. Subsequently, a diagnostic model based on multidimensional feature fusion is designed,… More >

Qiang Gao¹, Lei Shen^1,*, Jiaming Shi², Xinfa Gu², Shanyun Gu¹, Yuwei Ge¹, Yang Xie¹, Xiaoqiong Zhu¹, Baoguo Zang¹, Ming Zhang¹, Muhammad Shahzad Nazir², Jie Ji²

Energy Engineering, Vol.122, No.7, pp. 2767-2800, 2025, DOI:10.32604/ee.2025.065600 - 27 June 2025

Abstract The rapid proliferation of renewable energy integration and escalating grid operational complexity have intensified demands for resilient self-healing mechanisms in modern power systems. Conventional approaches relying on static models and heuristic rules exhibit limitations in addressing dynamic fault propagation and multi-modal data fusion. This study proposes a Transformer-enhanced intelligent microgrid self-healing framework that synergizes large language models (LLMs) with adaptive optimization, achieving three key innovations: (1) A hierarchical attention mechanism incorporating grid impedance characteristics for spatiotemporal feature extraction, (2) Dynamic covariance estimation Kalman filtering with wavelet packet energy entropy thresholds (Daubechies-4 basis, 6-level decomposition), and… More >

Umit Cigdem Turhal¹, Yasemin Onal^1,*, Kutalmis Turhal²

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2307-2332, 2025, DOI:10.32604/cmes.2025.064269 - 30 May 2025

Abstract The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other More > Graphic Abstract

Khaoula Ben Abdellafou¹, Kamel Zidi², Ahamed Aljuhani¹, Okba Taouali^1,*, Mohamed Faouzi Harkat³

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 3007-3023, 2025, DOI:10.32604/cmc.2025.063146 - 16 April 2025

Abstract Environmental protection requires identifying, investigating, and raising awareness about safeguarding nature from the harmful effects of both anthropogenic and natural events. This process of environmental protection is essential for maintaining human well-being. In this context, it is critical to monitor and safeguard the personal environment, which includes maintaining a healthy diet and ensuring plant safety. Living in a balanced environment and ensuring the safety of plants for green spaces and a healthy diet require controlling the nature and quality of the soil in our environment. To ensure soil quality, it is imperative to monitor and… More >

Yi-Feng Luo^1,*, Jyuan-Fong Yen², Wen-Cheng Su³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180 - 03 March 2025

Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

Feifei Yu¹, Yongxian Huang^2,*, Guoyan Chen¹, Xiaoqing Yang², Canyi Du^2,*, Yongkang Gong²

CMC-Computers, Materials & Continua, Vol.82, No.1, pp. 843-862, 2025, DOI:10.32604/cmc.2024.058051 - 03 January 2025

Abstract To accurately diagnose misfire faults in automotive engines, we propose a Channel Attention Convolutional Model, specifically the Squeeze-and-Excitation Networks (SENET), for classifying engine vibration signals and precisely pinpointing misfire faults. In the experiment, we established a total of 11 distinct states, encompassing the engine’s normal state, single-cylinder misfire faults, and dual-cylinder misfire faults for different cylinders. Data collection was facilitated by a highly sensitive acceleration signal collector with a high sampling rate of 20,840 Hz. The collected data were methodically divided into training and testing sets based on different experimental groups to ensure generalization and… More >

Alaa U. Khawaja¹, Ahmad Shaf^2,*, Faisal Al Thobiani³, Tariq Ali⁴, Muhammad Irfan⁵, Aqib Rehman Pirzada², Unza Shahkeel²

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.3, pp. 2399-2420, 2024, DOI:10.32604/cmes.2024.054257 - 31 October 2024

Abstract Electric motor-driven systems are core components across industries, yet they’re susceptible to bearing faults. Manual fault diagnosis poses safety risks and economic instability, necessitating an automated approach. This study proposes FTCNNLSTM (Fine-Tuned TabNet Convolutional Neural Network Long Short-Term Memory), an algorithm combining Convolutional Neural Networks, Long Short-Term Memory Networks, and Attentive Interpretable Tabular Learning. The model preprocesses the CWRU (Case Western Reserve University) bearing dataset using segmentation, normalization, feature scaling, and label encoding. Its architecture comprises multiple 1D Convolutional layers, batch normalization, max-pooling, and LSTM blocks with dropout, followed by batch normalization, dense layers, and More >

Rehman Ullah Khan¹, Fazal Shah^2,*, Ahmad Ali Khan³, Hamza Tahir²

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 345-367, 2024, DOI:10.32604/cmc.2024.054439 - 15 October 2024

Abstract Printed Circuit Boards (PCBs) are materials used to connect components to one another to form a working circuit. PCBs play a crucial role in modern electronics by connecting various components. The trend of integrating more components onto PCBs is becoming increasingly common, which presents significant challenges for quality control processes. Given the potential impact that even minute defects can have on signal traces, the surface inspection of PCB remains pivotal in ensuring the overall system integrity. To address the limitations associated with manual inspection, this research endeavors to automate the inspection process using the YOLOv8… More >