Keywords
Electrical fault diagnosis
Operation and maintenance
Property service electromechanical engineering
Operation and maintenance
Property service electromechanical engineering
Submitted : 2025-11-17
Accepted : 2025-12-02
Published : 2025-12-17
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
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[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.