Smart Sensor Networks for AI-Enabled Condition Monitoring in Electrical Systems

Abstract

Smart sensor networks, in conjunction with artificial intelligence (AI), represent a cutting-edge approach poised to revolutionize the realm of condition monitoring within electrical systems. This paper serves as a comprehensive exploration of the seamless integration of smart sensor networks and AI techniques, aimed at elevating the efficacy of monitoring the health and performance of electrical equipment. Each facet of these integrated systems is meticulously examined, beginning with the strategic deployment of sensors and progressing through the stages of data acquisition, preprocessing, and feature extraction. The pivotal role of AI-based analytics is elucidated, showcasing its capability to discern patterns, anomalies, and trends within the collected data, thereby facilitating fault detection and diagnostics with unparalleled precision. Furthermore, the paper delves into the realm of predictive maintenance, where AI-driven models leverage historical data to forecast potential failures and optimize maintenance schedules. The inherent benefits of employing smart sensor networks for AI-enabled condition monitoring are underscored, ranging from enhanced reliability and efficiency to reduced downtime and maintenance costs. However, amidst these benefits lie various challenges, including data quality concerns, interoperability issues, and the need for continuous improvement. Through a forward-looking lens, the paper extrapolates on the future directions of this field, envisaging advancements in edge computing, emerging AI techniques, and standardized protocols. Ultimately, this review aims to provide invaluable insights into the latest advancements in AI-enabled condition monitoring, guiding future research endeavors towards the realization of more efficient, reliable, and intelligent monitoring solutions for electrical systems.