Mohamed Zarouan¹, Ibrahim M. Mehedi^1,2,*, 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 experience and human knowledge, intellectual… More >

Xinliang Tang¹, Xiaotong Ru¹, Jingfang Su^1,*, 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 the detection accuracy, the Squeeze-and-Excitation… More >

So-Yeon Lee¹, Jihoon Park¹, Dae-Young Kim^2,*

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 devised a two-step data preprocessing… More >

Wangpeng He^1,*, 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. In the algorithm, a novel… More >

Gang Liu¹, Xinqi Chen^2,3,*, Lijuan Bao¹, Linbo Xu^2,3, Chaochao Dai¹, Lei Yang^2,3, 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 observer is preset. The fault… More >

Yousif Sufyan Jghef¹, Mohammed Jasim Mohammed Jasim², Subhi R. M. Zeebaree^3,*, 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 do so, the presented BIOMDL-FD… More >

Xunyou Zhang^1,2,*, Chuanyang Liu^1,3, 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 >

Xiaoyu Cheng¹, Mingxian Long¹, Wei He^1,2,*, 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 >

Fawad Nasim^1,2,*, Sohail Masood^1,2, Arfan Jaffar^1,2, 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 >

Kang Xiaofeng¹, Hu Kun^2,*, 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 >