IoT-Based Decision Support System for Health Monitoring of Induction Motors

Abstract

An electrical motor is a common device that is used for a variety of electrical purposes. Because of their wide range of applications, motors that are both reliable and long-lasting are in high demand. Motors are prone to a variety of faults, including rotor bar breaking faults, short turn faults, bearing outrace faults, and so on. Unexpected faults or failures in these motors reduce workplace productivity. The time it takes to resolve the issues reduces the organization’s profit. Bearing failures account for approximately 42% of all faults. Due to continuous operation, the shape of the majority of electrical motors with rolling bearings becomes disproportional. This causes the motor’s elastic limit to be exceeded, as well as fractures, vibrations, and a rise in temperature. A good solution is to switch from scheduled maintenance to predictive maintenance, which is based on monitoring the motor’s operating condition. This chapter proposes an Internet of Things (IoT)-based solution that continuously monitors and records the vibration from the induction motor. A decision support system analyzes the impact of vibration using log data and the Naïve Bayes classifier. The proposed decision support system detects the critical level of vibration and notifies the user of the motor’s abnormal working condition.