Application and Research of Electrical Fault Diagnosis and Operation and Maintenance Technology in Mechanical and Electrical Engineering of Property Services
Keywords:
Electrical fault diagnosis, Operation and maintenance, Property service electromechanical engineering
Abstract
This paper focuses on electrical fault diagnosis and operation and maintenance technology in property service electromechanical engineering. It details core diagnostic methods, application-oriented tools, predictive maintenance frameworks, and enhanced maintenance planning. It also explores wireless sensor networks, big data analytics, and design-phase applications. Case studies in construction and operation phases are presented. Challenges like legacy system retrofitting are noted, and future potential in quantum sensing and edge AI is discussed.
References
[1] Zhang Y, Li X, Wang H, et al., 2021, Intelligent Fault Diagnosis of Electrical Systems using Deep Neural Networks and IoT Data. IEEE Transactions on Industrial Informatics, 17(9): 6023–6032.
[2] Chen W, Liu Z, Zhang G, et al., 2022, Predictive Maintenance for Building Electrical Systems through PLC Signal Analysis. Automation in Construction, 2022(133): 104209.
[3] Wang Q, Xu J, Zhao Y, et al., 2023, BIM-Based Maintenance Planning for Electromechanical Systems in Smart Buildings. Advanced Engineering Informatics, 2023(56): 101986.
[4] Li H, Zhou Y, Zhang T, et al., 2021, Wireless Sensor Networks for Real-Time Monitoring of Electrical Parameters in HVAC Systems. Energy and Buildings, 2021(253): 111498.
[5] Zhou X, Liu S, Gao H, et al., 2020, Big Data Analytics Platform for Electrical Equipment Failure Prediction using Entropy Weight Method. Mechanical Systems and Signal Processing, 2020(143): 106847.
[6] Peng Y, Cheng J, Tang W, et al., 2022, Digital Twin-Driven Energy Management for Building Electrical Systems. Applied Energy, 2022(311): 118676.
[7] Sun L, Jiang P, Wen C, et al., 2023, AI-Powered Fault Knowledge Base for Electromechanical System Maintenance. Expert Systems with Applications, 2023(214): 119134.
[8] Zhang G, Li S, Wang F, et al., 2021, Infrared Thermography and Neural Networks for Electrical Fault Detection. Measurement Science and Technology, 32(3): 35104.
[9] Wu D, Zhang K, Liu Y, et al., 2023, IoT-Enabled Predictive Maintenance Framework for Property Service Electrical Systems. Building and Environment, 2023(228): 109872.
[10] Xu M, Li J, Zhang Y, et al., 2020, Multi-Objective Optimization Model for Energy-Efficient Electrical Equipment Selection. Energy Conversion and Management, 2020(225): 113456.
[11] Liu X, Wang Z, Chen D, et al., 2021, Augmented Reality for Electrical Installation Guidance in Construction Projects. Automation in Construction, 2021(128): 103767.
[12] Hu R, Yang C, Zhang W, et al. 2022, RFID-Based Cable Management System for Electrical Construction Projects. Advanced Engineering Informatics, 2022(54): 101730.
[13] Zhao B, Xu W, Li M, et al., 2023, Quantum Sensing Applications in Electrical System Monitoring. Sensors and Actuators A: Physical, 2023(352): 114202.
[14] Feng C, Liang Y, Zhou T, et al., 2022, Edge AI Implementation for Real-Time Electrical Fault Detection. IEEE Internet of Things Journal, 9(18): 17645–17655.
[15] Guo S, Wang J, Cheng X, et al., 2023, Machine Learning-Based Automated CAD Generation for Electrical System Design. Computer-Aided Design, 2023(154): 103421.
[2] Chen W, Liu Z, Zhang G, et al., 2022, Predictive Maintenance for Building Electrical Systems through PLC Signal Analysis. Automation in Construction, 2022(133): 104209.
[3] Wang Q, Xu J, Zhao Y, et al., 2023, BIM-Based Maintenance Planning for Electromechanical Systems in Smart Buildings. Advanced Engineering Informatics, 2023(56): 101986.
[4] Li H, Zhou Y, Zhang T, et al., 2021, Wireless Sensor Networks for Real-Time Monitoring of Electrical Parameters in HVAC Systems. Energy and Buildings, 2021(253): 111498.
[5] Zhou X, Liu S, Gao H, et al., 2020, Big Data Analytics Platform for Electrical Equipment Failure Prediction using Entropy Weight Method. Mechanical Systems and Signal Processing, 2020(143): 106847.
[6] Peng Y, Cheng J, Tang W, et al., 2022, Digital Twin-Driven Energy Management for Building Electrical Systems. Applied Energy, 2022(311): 118676.
[7] Sun L, Jiang P, Wen C, et al., 2023, AI-Powered Fault Knowledge Base for Electromechanical System Maintenance. Expert Systems with Applications, 2023(214): 119134.
[8] Zhang G, Li S, Wang F, et al., 2021, Infrared Thermography and Neural Networks for Electrical Fault Detection. Measurement Science and Technology, 32(3): 35104.
[9] Wu D, Zhang K, Liu Y, et al., 2023, IoT-Enabled Predictive Maintenance Framework for Property Service Electrical Systems. Building and Environment, 2023(228): 109872.
[10] Xu M, Li J, Zhang Y, et al., 2020, Multi-Objective Optimization Model for Energy-Efficient Electrical Equipment Selection. Energy Conversion and Management, 2020(225): 113456.
[11] Liu X, Wang Z, Chen D, et al., 2021, Augmented Reality for Electrical Installation Guidance in Construction Projects. Automation in Construction, 2021(128): 103767.
[12] Hu R, Yang C, Zhang W, et al. 2022, RFID-Based Cable Management System for Electrical Construction Projects. Advanced Engineering Informatics, 2022(54): 101730.
[13] Zhao B, Xu W, Li M, et al., 2023, Quantum Sensing Applications in Electrical System Monitoring. Sensors and Actuators A: Physical, 2023(352): 114202.
[14] Feng C, Liang Y, Zhou T, et al., 2022, Edge AI Implementation for Real-Time Electrical Fault Detection. IEEE Internet of Things Journal, 9(18): 17645–17655.
[15] Guo S, Wang J, Cheng X, et al., 2023, Machine Learning-Based Automated CAD Generation for Electrical System Design. Computer-Aided Design, 2023(154): 103421.
Published
2025-12-16
Section
Articles
