IoT-Enabled Predictive Maintenance of Electrical Machines Using Edge Intelligence

Amit Rajendrabhai Pathak; Hardik Maheshbhai Pandya; Alpesh Sureshchandra Adeshara; Anoop Harishbhai Budharani; Devendra Bhanuprasad Raval

Amit Rajendrabhai Pathak Assistant Professor, Electrical Engineering Department, V.V.P. Engineering College, Rajkot, Gujarat Technological University, Gujarat
Hardik Maheshbhai Pandya Assistant Professor, Electrical Engineering Department, V.V.P. Engineering College, Rajkot, Gujarat Technological University, Gujarat
Alpesh Sureshchandra Adeshara Professor & Head, Electrical Engineering Department, V.V.P. Engineering College, Rajkot, Gujarat Technological University, Gujarat
Anoop Harishbhai Budharani Assistant Professor, Electrical Engineering Department, V.V.P. Engineering College, Rajkot, Gujarat Technological University, Gujarat
Devendra Bhanuprasad Raval Lab Assistant, Electrical Engineering Department, V.V.P. Engineering College, Rajkot, Gujarat Technological University, Gujarat

Abstract

Electrical machine predictive maintenance is essential for lowering operating expenses, increasing equipment longevity, and avoiding downtime. Traditional maintenance techniques have changed to more intelligent, data-driven methods with the introduction of the Internet of Things (IoT). This study investigates a conceptual framework for an Internet of Things-enabled predictive maintenance system that uses edge intelligence to continuously monitor and assess the condition of electrical machinery. By processing sensor data (such as vibration, temperature, and current anomalies) near the source using local edge computing, the suggested architecture allows for quick defect prediction without depending on cloud delay. The paper outlines real-world applications in motors and transformers while reviewing recent developments in edge computing and the Internet of Things as they relate to electrical systems. The technical advantages, security ramifications, and prospects of implementing intelligent, scalable, and energy-efficient maintenance systems in industrial settings are also covered in the study. This contribution aims to provide a roadmap for the implementation of advanced, next generation maintenance systems that align with the objectives of Industry 4.0.