AI Based Electrical Safety and Predictive Maintenance System

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Year : 2026 | Volume : 13 | 02 | Page :
By

Jabash Samuel,

R V. Aswin,

B. Aakash,

T. Acsilin Birno,

S. Jefrin Jithesh,

  1. Associate Professor, Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Tamil Nadu, India
  2. Student, Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Tamil Nadu, India
  3. Student, Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Tamil Nadu, India
  4. Student, Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Tamil Nadu, India
  5. Student, Department of Electrical and Electronics Engineering, Rohini College of Engineering and Technology, Tamil Nadu, India

Abstract

This review examines twenty research papers on artificial intelligence (AI), Internet of Things (IoT), condition monitoring, fault diagnosis and predictive maintenance, with emphasis on their relevance to electrical safety. Electrical equipment such as induction motors, contactors, transformers, batteries and renewable-energy systems can develop abnormal conditions that lead to unexpected shutdowns, equipment damage or unsafe operation. Conventional maintenance based only on fixed schedules or after-failure repair may not identify every developing fault in time. The reviewed studies investigate how sensor and operational data can be processed to recognize anomalies, classify faults and estimate remaining useful life (RUL). The techniques covered include Support Vector Machines, Random Forest, AdaBoost, fuzzy logic, K-Means clustering, convolutional neural networks, Long Short-Term Memory networks, CNN-LSTM models, Transformer-based methods and generative adversarial learning. Data sources include vibration signals, voltage and current measurements, temperature, machine-state records, SCADA information and battery ageing features. Several studies report promising outcomes, including high fault-detection accuracy, improved RUL estimates and better identification of abnormal operating patterns. However, performance depends on data quality, representative fault examples, sensor reliability and the ability of a model to adapt to changing operating conditions. This review compares the methods and reported findings, discusses implementation challenges, and identifies practical design considerations for an AI-based electrical safety and predictive-maintenance system. Such a system can combine continuous sensing, intelligent analysis and early alerts to support safer operation and more informed maintenance planning.

Keywords: Artificial Intelligence (AI); Electrical Safety; Predictive Maintenance; Fault Detection and Diagnosis; Internet of Things (IoT)

How to cite this article: Jabash Samuel, R V. Aswin, B. Aakash, T. Acsilin Birno, S. Jefrin Jithesh. AI Based Electrical Safety and Predictive Maintenance System. Recent Trends in Sensor Research & Technology. 2026; 13(02):-.
How to cite this URL: Jabash Samuel, R V. Aswin, B. Aakash, T. Acsilin Birno, S. Jefrin Jithesh. AI Based Electrical Safety and Predictive Maintenance System. Recent Trends in Sensor Research & Technology. 2026; 13(02):-. Available from: https://journals.stmjournals.com/rtsrt/article=2026/view=261223

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Ahead of Print Subscription Review Article
Volume 13
02
Received 14/10/2026
Accepted 19/10/2026
Published 22/10/2026
Publication Time 8 Days

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