Predictive Maintenance of Electrical Machines using Machine Learning and Condition Monitoring Data

Predictive maintenance improves electrical machinery performance and reliability across industries. Machine learning algorithms combined with real-time condition monitoring data reduce unscheduled delays, enable proactive maintenance planning, and predict future faults.This research describes predictive electrical equipment maintenance using machine learning and condition monitoring data. The suggested method involves data acquisition and preparation, feature extraction and selection, and model construction and deployment.Data preparation and gathering involve real-time measurement from electrical equipment sensors and processing to remove noise and outliers. Numerous methods are used to gain insights from raw sensor data. Include time-series analysis, signal processing, and feature engineering.In the feature extraction and selection phase, meaningful features are extracted from the preprocessed data to capture latent patterns that signal machine health. Feature selection methods like RFE, PCA, and correlation analysis can find predictive features.Before building and deploying predictive maintenance models, SVMs, neural networks, and random forests are trained on the chosen features. This research shows that predictive maintenance solutions that incorporate machine learning and condition monitoring data can benefit electrical equipment. Advanced analytics and real-time monitoring can boost efficiency, reliability, and cost savings in sectors.