Smart Sensor Networks for Al-Powered Condition Monitoring in Electrical Systems

Abstract

Smart sensor networks have revolutionized condition monitoring in electrical systems by integrating artificial intelligence (AI) algorithms. These systems enable real-time monitoring, analysis, and prediction of equipment health, enhancing reliability and reducing downtime. This paper presents an overview of smart sensor networks for AI-powered condition monitoring in electrical systems. It discusses the integration of advanced sensors with AI algorithms to detect and diagnose faults, predict failures, and optimize maintenance strategies. The application of machine learning and deep learning techniques enables the extraction of valuable insights from sensor data, facilitating proactive maintenance and decision-making. Case studies and practical implementations illustrate the effectiveness of smart sensor networks in improving the performance and efficiency of electrical systems. Overall, smart sensor networks offer a promising approach to enhance condition monitoring in electrical systems, paving the way for predictive maintenance and optimized asset management strategies.