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  • Open Access


    Optimizing Optical Fiber Faults Detection: A Comparative Analysis of Advanced Machine Learning Approaches

    Kamlesh Kumar Soothar1,2, Yuanxiang Chen1,2,*, Arif Hussain Magsi3, Cong Hu1, Hussain Shah1

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2697-2721, 2024, DOI:10.32604/cmc.2024.049607

    Abstract Efficient optical network management poses significant importance in backhaul and access network communication for preventing service disruptions and ensuring Quality of Service (QoS) satisfaction. The emerging faults in optical networks introduce challenges that can jeopardize the network with a variety of faults. The existing literature witnessed various partial or inadequate solutions. On the other hand, Machine Learning (ML) has revolutionized as a promising technique for fault detection and prevention. Unlike traditional fault management systems, this research has three-fold contributions. First, this research leverages the ML and Deep Learning (DL) multi-classification system and evaluates their accuracy… More >

  • Open Access


    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

    Sheetal Sharma1,2, Kamali Gupta1, Deepali Gupta1, Shalli Rani1,*, Gaurav Dhiman3,4,5,6,7,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997

    Abstract The widespread adoption of the Internet of Things (IoT) has transformed various sectors globally, making them more intelligent and connected. However, this advancement comes with challenges related to the effectiveness of IoT devices. These devices, present in offices, homes, industries, and more, need constant monitoring to ensure their proper functionality. The success of smart systems relies on their seamless operation and ability to handle faults. Sensors, crucial components of these systems, gather data and contribute to their functionality. Therefore, sensor faults can compromise the system’s reliability and undermine the trustworthiness of smart environments. To address… More > Graphic Abstract

    An Insight Survey on Sensor Errors and Fault Detection Techniques in Smart Spaces

  • Open Access


    Gradient Optimizer Algorithm with Hybrid Deep Learning Based Failure Detection and Classification in the Industrial Environment

    Mohamed Zarouan1, Ibrahim M. Mehedi1,2,*, Shaikh Abdul Latif3, Md. Masud Rana4

    CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.2, pp. 1341-1364, 2024, DOI:10.32604/cmes.2023.030037

    Abstract Failure detection is an essential task in industrial systems for preventing costly downtime and ensuring the seamless operation of the system. Current industrial processes are getting smarter with the emergence of Industry 4.0. Specifically, various modernized industrial processes have been equipped with quite a few sensors to collect process-based data to find faults arising or prevailing in processes along with monitoring the status of processes. Fault diagnosis of rotating machines serves a main role in the engineering field and industrial production. Due to the disadvantages of existing fault, diagnosis approaches, which greatly depend on professional… More >

  • Open Access


    A Transmission and Transformation Fault Detection Algorithm Based on Improved YOLOv5

    Xinliang Tang1, Xiaotong Ru1, Jingfang Su1,*, Gabriel Adonis2

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 2997-3011, 2023, DOI:10.32604/cmc.2023.038923

    Abstract On the transmission line, the invasion of foreign objects such as kites, plastic bags, and balloons and the damage to electronic components are common transmission line faults. Detecting these faults is of great significance for the safe operation of power systems. Therefore, a YOLOv5 target detection method based on a deep convolution neural network is proposed. In this paper, Mobilenetv2 is used to replace Cross Stage Partial (CSP)-Darknet53 as the backbone. The structure uses depth-wise separable convolution toreduce the amount of calculation and parameters; improve the detection rate. At the same time, to compensate for… More >

  • Open Access


    Context Awareness by Noise-Pattern Analysis of a Smart Factory

    So-Yeon Lee1, Jihoon Park1, Dae-Young Kim2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 1497-1514, 2023, DOI:10.32604/cmc.2023.034914

    Abstract Recently, to build a smart factory, research has been conducted to perform fault diagnosis and defect detection based on vibration and noise signals generated when a mechanical system is driven using deep-learning technology, a field of artificial intelligence. Most of the related studies apply various audio-feature extraction techniques to one-dimensional raw data to extract sound-specific features and then classify the sound by using the derived spectral image as a training dataset. However, compared to numerical raw data, learning based on image data has the disadvantage that creating a training dataset is very time-consuming. Therefore, we… More >

  • Open Access


    Sparsity-Enhanced Model-Based Method for Intelligent Fault Detection of Mechanical Transmission Chain in Electrical Vehicle

    Wangpeng He1,*, Yue Zhou1, Xiaoya Guo2, Deshun Hu1, Junjie Ye3

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2495-2511, 2023, DOI:10.32604/cmes.2023.027896

    Abstract In today’s world, smart electric vehicles are deeply integrated with smart energy, smart transportation and smart cities. In electric vehicles (EVs), owing to the harsh working conditions, mechanical parts are prone to fatigue damages, which endanger the driving safety of EVs. The practice has proved that the identification of periodic impact characteristics (PICs) can effectively indicate mechanical faults. This paper proposes a novel model-based approach for intelligent fault diagnosis of mechanical transmission train in EVs. The essential idea of this approach lies in the fusion of statistical information and model information from a dynamic process.… More >

  • Open Access


    Weak Fault Detection of Rotor Winding Inter-Turn Short Circuit in Excitation System Based on Residual Interval Observer

    Gang Liu1, Xinqi Chen2,3,*, Lijuan Bao1, Linbo Xu2,3, Chaochao Dai1, Lei Yang2,3, Chengmin Wang4

    Structural Durability & Health Monitoring, Vol.17, No.4, pp. 337-351, 2023, DOI:10.32604/sdhm.2022.023583

    Abstract Aiming at the fact that the rotor winding inter-turn weak faults can hardly be detected due to the strong electromagnetic coupling effect in the excitation system, an interval observer based on current residual is designed. Firstly, the mechanism of the inter-turn short circuit of the rotor winding in the excitation system is modeled under the premise of stable working conditions, and electromagnetic decoupling and system simplification are carried out through Park Transform. An interval observer is designed based on the current residual in the two-phase coordinate system, and the sensitive and stable conditions of the… More >

  • Open Access


    Billiards Optimization with Modified Deep Learning for Fault Detection in Wireless Sensor Network

    Yousif Sufyan Jghef1, Mohammed Jasim Mohammed Jasim2, Subhi R. M. Zeebaree3,*, Rizgar R. Zebari4

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 1651-1664, 2023, DOI:10.32604/csse.2023.037449

    Abstract Wireless Sensor Networks (WSNs) gather data in physical environments, which is some type. These ubiquitous sensors face several challenges responsible for corrupting them (mostly sensor failure and intrusions in external agents). WSNs were disposed to error, and effectual fault detection techniques are utilized for detecting faults from WSNs in a timely approach. Machine learning (ML) was extremely utilized for detecting faults in WSNs. Therefore, this study proposes a billiards optimization algorithm with modified deep learning for fault detection (BIOMDL-FD) in WSN. The BIOMDLFD technique mainly concentrates on identifying sensor faults to enhance network efficiency. To… More >

  • Open Access


    Line Fault Detection of DC Distribution Networks Using the Artificial Neural Network

    Xunyou Zhang1,2,*, Chuanyang Liu1,3, Zuo Sun1

    Energy Engineering, Vol.120, No.7, pp. 1667-1683, 2023, DOI:10.32604/ee.2023.025186

    Abstract A DC distribution network is an effective solution for increasing renewable energy utilization with distinct benefits, such as high efficiency and easy control. However, a sudden increase in the current after the occurrence of faults in the network may adversely affect network stability. This study proposes an artificial neural network (ANN)-based fault detection and protection method for DC distribution networks. The ANN is applied to a classifier for different faults on the DC line. The backpropagation neural network is used to predict the line current, and the fault detection threshold is obtained on the basis More >

  • Open Access


    Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

    Xiaoyu Cheng1, Mingxian Long1, Wei He1,2,*, Hailong Zhu1

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2821-2844, 2023, DOI:10.32604/csse.2023.037330

    Abstract Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. More >

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