Machine Learning

Industrial Equipment Failure Prediction Using Machine Learning

The Industrial Equipment Failure Prediction Using Machine Learning system is a comprehensive machine learning solution that predicts industrial equipment failures using sensor data. This project is ideal for B.Tech, MCA, CSE, and engineering students as their final year project. It implements Lasso (L1) and Ridge (L2) Logistic Regression with leak-free SMOTE, achieving 96.6% accuracy and 0.9785 ROC-AUC.

The system utilizes the Predictive Maintenance Dataset containing 10,000 equipment records with 9 key features including temperature measurements, rotational speed, torque, and tool wear. It provides a user-friendly web interface for data upload, exploratory data analysis, model training, and real-time equipment failure prediction, enabling proactive maintenance scheduling and reduced industrial downtime.

• Multi-Model Failure Prediction
• Ridge Logistic (96.6% Accuracy)
• Lasso Logistic (96.2% Accuracy)
• Feature Engineering (Temperature Difference, Power, Risk Score)
• Leak-free SMOTE Cross-Validation
• Interactive Visualizations
• Real-time Failure Predictions
• Model Performance Comparison
• Feature Importance Analysis
• Flask Web Application