Research on Intelligent Monitoring and Fault Diagnosis of Electric Motors in Thermal Power Plants Based on Big Data Analysis

This paper investigates intelligent monitoring and fault diagnosis technologies for electric motors in thermal power plants based on big data analysis. In response to the deficiencies of traditional maintenance models, such as high maintenance costs and untimely fault diagnosis, this study proposes a deep learning model based on Long Short-Term Memory (LSTM) networks for real-time monitoring of the operating status of electric motors and fault diagnosis. By installing various intelligent sensors (such as voltage, current, temperature, and vibration sensors) on the electric motors, operating parameters are collected and analyzed using big data techniques to achieve real-time monitoring of the motor status and early warning of faults. The research not only improves the operational reliability of electric motors in thermal power plants and reduces maintenance costs, but also provides valuable references for equipment condition monitoring and fault diagnosis in other industrial fields.