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 >

Kamlesh Kumar Soothar^1,2, Yuanxiang Chen^1,2,*, 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 - 15 May 2024

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 >

Hang Cui¹, Hongbo Ren^1,*, Qiong Wu^1,2, Hang Lv¹, Qifen Li^1,2, Weisheng Zhou³

Energy Engineering, Vol.121, No.3, pp. 581-601, 2024, DOI:10.32604/ee.2023.047470 - 27 February 2024

Abstract Cascading faults have been identified as the primary cause of multiple power outages in recent years. With the emergence of integrated energy systems (IES), the conventional approach to analyzing power grid cascading faults is no longer appropriate. A cascading fault analysis method considering multi-energy coupling characteristics is of vital importance. In this study, an innovative analysis method for cascading faults in integrated heat and electricity systems (IHES) is proposed. It considers the degradation characteristics of transmission and energy supply components in the system to address the impact of component aging on cascading faults. Firstly, degradation… More >

Sheetal Sharma^1,2, Kamali Gupta¹, Deepali Gupta¹, Shalli Rani^1,*, Gaurav Dhiman^3,4,5,6,7,*

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.3, pp. 2029-2059, 2024, DOI:10.32604/cmes.2023.029997 - 15 December 2023

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

Youping Lin¹, Huangbin Lin^2,*, Dezhi Wei¹

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 1363-1379, 2023, DOI:10.32604/iasc.2023.038601 - 21 June 2023

Abstract The digital twin (DT) includes real-time data analytics based on the actual product or manufacturing processing parameters. Data from digital twins can predict asset maintenance requirements ahead of time. This saves money by decreasing operating expenses and asset downtime, which improves company efficiency. In this paper, a digital twin in braiding machinery based on IoT (DTBM-IoT) used to diagnose faults. When an imbalance fault occurs, the system gathers experimental data. After that, the information is sent into a digital win model of the rotor system to see whether it can quantify and locate imbalance for More >

Tarique Rafique Memon^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain Kalwar⁵, Bhawani Shankar Chowdhry¹

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2461-2477, 2023, DOI:10.32604/cmc.2023.035413 - 31 March 2023

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… More >

Quanchun Yan^1,2,*, Chao Yuan¹, Wen Gu¹, Yanan Liu¹, Yiming Tang¹

Energy Engineering, Vol.120, No.1, pp. 49-68, 2023, DOI:10.32604/ee.2022.018555 - 27 October 2022

Abstract In order to solve the problems of rotor overvoltage, overcurrent and DC side voltage rise caused by grid voltage drops, a coordinated control strategy based on symmetrical and asymmetrical low voltage ride through of rotor side converter of the doubly-fed generator is proposed. When the power grid voltage drops symmetrically, the generator approximate equation under steady-state conditions is no longer applicable. Considering the dynamic process of stator current excitation, according to the change of stator flux and the depth of voltage drop, the system can dynamically provide reactive power support for parallel nodes and suppress… More >

Majid Hussain^1,2,*, Tayab Din Memon^3,4, Imtiaz Hussain⁵, Zubair Ahmed Memon³, Dileep Kumar²

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.2, pp. 435-470, 2022, DOI:10.32604/cmes.2022.020583 - 21 July 2022

Abstract Owing to the 4.0 industrial revolution condition monitoring maintenance is widely accepted as a useful approach to avoiding plant disturbances and shutdown. Recently, Motor Current Signature Analysis (MCSA) is widely reported as a condition monitoring technique in the detection and identification of individual and multiple Induction Motor (IM) faults. However, checking the fault detection and classification with deep learning models and its comparison among themselves or conventional approaches is rarely reported in the literature. Therefore, in this work, we present the detection and identification of induction motor faults with MCSA and three Deep Learning (DL)… More >

Lida Liao¹, Bo Xiao^1,2,*, Kan Huang^1,*, Bin Huang^1,3, Kang Zhang¹

Energy Engineering, Vol.119, No.3, pp. 1219-1238, 2022, DOI:10.32604/ee.2022.017471 - 31 March 2022

Abstract With the increasingly stringent requirements for carbon emissions, countries have increased the scale of clean energy use in recent years. As an important new clean energy source, the ratio of wind power in energy utilization has been increasing. The horizontal axis wind turbine is the main form of wind power generation, which is subject to random wind loads during operation and is prone to various failures after a long period of operation, resulting in reduced power generation efficiency or even shutdown. In order to ensure stable external power transmission, it is necessary to perform fault… More >

Gysbert Basson^1,*, Andrew P. Bassom², Brian Salmon³

CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 379-409, 2022, DOI:10.32604/cmes.2022.019112 - 24 January 2022

Abstract Advances in numerical simulation techniques play an important role in helping mining engineers understand those parts of the rock mass that cannot be readily observed. The Material Point Method (MPM) is an example of such a tool that is gaining popularity for studying geotechnical problems. In recent years, the original formulation of MPM has been extended to not only account for simulating the mechanical behaviour of rock under different loading conditions, but also to describe the coupled interaction of pore water and solid phases in materials. These methods assume that the permeability of mediums is… More >