Home / Current Issue / Paper 1717727
AI-Based Predictive Fault Detection for Smart Electrical Equipment Using IoT and Machine Learning
Subject area: Arts, Social Sciences and Humanities · Area of research: AI-Based
DOI: https://doi.org/10.64388/IREV9I11-1717727
Abstract
Smart electrical equipment such as transformers, induction motors, generators, and industrial automation systems are critical components in modern industries. Unexpected failures in these systems can cause production interruptions, increased maintenance cost, equipment degradation, and safety hazards. Traditional maintenance strategies, including reactive and preventive maintenance, are often inefficient because they either respond only after failures occur or rely on fixed maintenance schedules without considering real-time equipment conditions. To address these limitations, this paper proposes an AI-driven self-learning failure prediction system for smart electrical equipment using IoT-enabled monitoring and machine learning techniques.The proposed system continuously acquires real-time operational parameters such as temperature and current using embedded sensors integrated with a microcontroller-based edge computing unit. The collected sensor data is processed using machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to identify abnormal operating conditions and predict potential equipment failures. The system incorporates a self-learning mechanism that updates the prediction model using newly acquired operational data, thereby improving fault detection accuracy over time. An IoT-based communication framework provides real-time monitoring and instant fault alerts through cloud-connected applications.
Keywords
Predictive Maintenance, Artificial Intelligence, Internet of Things (IoT), Machine Learning, Smart Electrical Equipment, Failure Prediction, LSTM, Random Forest.
References
[1] C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995, doi: 10.1007/BF00994018.
[2] L. Breiman, “Random forests,” Machine Learning, vol. 45, no. 1, pp. 5–32, 2001, doi: 10.1023/A:1010933404324.
[3] S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735–1780, 1997, doi: 10.1162/neco.1997.9.8.1735.
[4] A. Widodo and B. S. Yang, “Support vector machine in machine condition monitoring and fault diagnosis,” Mechanical Systems and Signal Processing, vol. 21, no. 6, pp. 2560–2574, 2007, doi: 10.1016/j.ymssp.2006.12.007.
[5] R. Zhao, R. Yan, Z. Chen, K. Mao, P. Wang, and R. Gao, “Deep learning and its applications to machine health monitoring,” Mechanical Systems and Signal Processing, vol. 115, pp. 213–237, 2019, doi: 10.1016/j.ymssp.2018.05.050.
[6] J. Lee, H. Davari, J. Singh, and V. Pandhare, “Industrial AI and predictive analytics for smart manufacturing systems,” Manufacturing Letters, vol. 18, pp. 20–23, 2018, doi: 10.1016/j.mfglet.2018.09.002.
[7] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” IEEE Internet of Things Journal, vol. 3, no. 5, pp. 637–646, 2016, doi: 10.1109/JIOT.2016.2579198.
[8] F. Tao, Q. Qi, L. Wang, and A. Y. C. Nee, “Digital twins and cyber–physical systems toward smart manufacturing and Industry 4.0,” Engineering, vol. 5, no. 4, pp. 653–661, 2019, doi: 10.1016/j.eng.2019.01.014.
[9] W. Zhang, G. Peng, C. Li, Y. Chen, and Z. Zhang, “A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals,” Sensors, vol. 17, no. 2, p. 425, 2017, doi: 10.3390/s17020425.
[10] S. Yin, H. Luo, and S. X. Ding, “Real-time implementation of fault-tolerant control systems with performance optimization,” IEEE Transactions on Industrial Electronics, vol. 61, no. 5, pp. 2402–2411, 2014, doi: 10.1109/TIE.2013.2270213.
How to cite this paper
@article{1717727,
author = {Sumathi G, Muthukumar M, Balachandran C, S. Dharun Prabhu},
title = {AI-Based Predictive Fault Detection for Smart Electrical Equipment Using IoT and Machine Learning},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {11},
pages = {1780-1787},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1717727.pdf},
abstract = {Smart electrical equipment such as transformers, induction motors, generators, and industrial automation systems are critical components in modern industries. Unexpected failures in these systems can cause production interruptions, increased maintenance cost, equipment degradation, and safety hazards. Traditional maintenance strategies, including reactive and preventive maintenance, are often inefficient because they either respond only after failures occur or rely on fixed maintenance schedules without considering real-time equipment conditions. To address these limitations, this paper proposes an AI-driven self-learning failure prediction system for smart electrical equipment using IoT-enabled monitoring and machine learning techniques.The proposed system continuously acquires real-time operational parameters such as temperature and current using embedded sensors integrated with a microcontroller-based edge computing unit. The collected sensor data is processed using machine learning algorithms including Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks to identify abnormal operating conditions and predict potential equipment failures. The system incorporates a self-learning mechanism that updates the prediction model using newly acquired operational data, thereby improving fault detection accuracy over time. An IoT-based communication framework provides real-time monitoring and instant fault alerts through cloud-connected applications.},
keywords = {Predictive Maintenance, Artificial Intelligence, Internet of Things (IoT), Machine Learning, Smart Electrical Equipment, Failure Prediction, LSTM, Random Forest.},
month = {May},
doi = {https://doi.org/10.64388/IREV9I11-1717727}
}