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  • 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 of the difference between the… 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. Through fault tree analysis (FTA),… More >

  • Open Access


    Intelligent Sound-Based Early Fault Detection System for Vehicles

    Fawad Nasim1,2,*, Sohail Masood1,2, Arfan Jaffar1,2, Usman Ahmad1, Muhammad Rashid3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3175-3190, 2023, DOI:10.32604/csse.2023.034550

    Abstract An intelligent sound-based early fault detection system has been proposed for vehicles using machine learning. The system is designed to detect faults in vehicles at an early stage by analyzing the sound emitted by the car. Early detection and correction of defects can improve the efficiency and life of the engine and other mechanical parts. The system uses a microphone to capture the sound emitted by the vehicle and a machine-learning algorithm to analyze the sound and detect faults. A possible fault is determined in the vehicle based on this processed sound. Binary classification is done at the first stage… More >

  • Open Access


    Acoustic Emission Recognition Based on a Three-Streams Neural Network with Attention

    Kang Xiaofeng1, Hu Kun2,*, Ran Li3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2963-2974, 2023, DOI:10.32604/csse.2023.025908

    Abstract Acoustic emission (AE) is a nondestructive real-time monitoring technology, which has been proven to be a valid way of monitoring dynamic damage to materials. The classification and recognition methods of the AE signals of the rotor are mostly focused on machine learning. Considering that the huge success of deep learning technologies, where the Recurrent Neural Network (RNN) has been widely applied to sequential classification tasks and Convolutional Neural Network (CNN) has been widely applied to image recognition tasks. A novel three-streams neural network (TSANN) model is proposed in this paper to deal with fault detection tasks. Based on residual connection… More >

  • Open Access


    Embedded System Development for Detection of Railway Track Surface Deformation Using Contour Feature Algorithm

    Tarique Rafique Memon1,2,*, Tayab Din Memon3,4, Imtiaz Hussain Kalwar5, Bhawani Shankar Chowdhry1

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2461-2477, 2023, DOI:10.32604/cmc.2023.035413

    Abstract Derailment of trains is not unusual all around the world, especially in developing countries, due to unidentified track or rolling stock faults that cause massive casualties each year. For this purpose, a proper condition monitoring system is essential to avoid accidents and heavy losses. Generally, the detection and classification of railway track surface faults in real-time requires massive computational processing and memory resources and is prone to a noisy environment. Therefore, in this paper, we present the development of a novel embedded system prototype for condition monitoring of railway track. The proposed prototype system works in real-time by acquiring railway… More >

  • Open Access


    Novel Metrics for Mutation Analysis

    Savas Takan1,*, Gokmen Katipoglu2

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2075-2089, 2023, DOI:10.32604/csse.2023.036791

    Abstract A measure of the “goodness” or efficiency of the test suite is used to determine the proficiency of a test suite. The appropriateness of the test suite is determined through mutation analysis. Several Finite State Machine (FSM) mutants are produced in mutation analysis by injecting errors against hypotheses. These mutants serve as test subjects for the test suite (TS). The effectiveness of the test suite is proportional to the number of eliminated mutants. The most effective test suite is the one that removes the most significant number of mutants at the optimal time. It is difficult to determine the fault… More >

  • Open Access


    A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

    Chen Chen1,2, Xingqiu Li2,3,*, Kai Huang4, Zhongwei Xu1, Meng Mei1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033

    Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data pre-processing and 2D representation. Next,… More >

  • Open Access


    Method for Fault Diagnosis and Speed Control of PMSM

    Smarajit Ghosh*

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2391-2404, 2023, DOI:10.32604/csse.2023.028931

    Abstract In the field of fault tolerance estimation, the increasing attention in electrical motors is the fault detection and diagnosis. The tasks performed by these machines are progressively complex and the enhancements are likewise looked for in the field of fault diagnosis. It has now turned out to be essential to diagnose faults at their very inception; as unscheduled machine downtime can upset deadlines and cause heavy financial burden. In this paper, fault diagnosis and speed control of permanent magnet synchronous motor (PMSM) is proposed. Elman Neural Network (ENN) is used to diagnose the fault of permanent magnet synchronous motor. Both… More >

  • Open Access


    Transformer Internal and Inrush Current Fault Detection Using Machine Learning

    R. Vidhya1,*, P. Vanaja Ranjan2, N. R. Shanker3

    Intelligent Automation & Soft Computing, Vol.36, No.1, pp. 153-168, 2023, DOI:10.32604/iasc.2023.031942

    Abstract Preventive maintenance in the transformer is performed through a differential relay protection system, and it protects the transformer from internal and external faults. However, the Current Transformer (CT) in the differential protection system mal-operates during inrush currents. CT saturates due to magnetizing inrush currents and causes false tripping of the differential relays. Moreover, identification of tripping in protection relay either due to inrush current or internal faults needs to be diagnosed. For the above problem, continuous monitoring of transformer breather and CT terminals with thermal camera helps detect the tripping in relay due to inrush or internal fault. The transformer’s… More >

  • Open Access


    Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

    Farrukh Shahzad Ahmed, Awais Majeed, Tamim Ahmed Khan*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2211-2238, 2023, DOI:10.32604/cmc.2023.032664

    Abstract Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric for performance evaluation of TCP… More >

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