A machine learning system for predictive maintenance, anomaly detection, and Remaining Useful Life (RUL) prediction for industrial machines using time-series sensor data.
This project uses LSTM Autoencoder networks to learn normal behavior patterns of machine sensors and detect anomalies that may indicate potential machine failures.
Modern industries rely on predictive maintenance to reduce downtime and avoid catastrophic machine failures. Traditional maintenance strategies like reactive or scheduled maintenance are inefficient.
This project implements a data-driven predictive maintenance system capable of:
• Detecting abnormal machine behavior
• Predicting machine failure patterns
• Estimating Remaining Useful Life (RUL)
The model is trained using sensor time-series data from machine bearings and identifies anomalies using reconstruction error from an LSTM Autoencoder.
- Time-series sensor data processing
- LSTM Autoencoder architecture
- Anomaly detection using reconstruction loss
- Remaining Useful Life (RUL) estimation
- Data preprocessing and feature scaling
- Sliding window sequence generation
- Visualization of anomalies and predictions
The project uses the IMS Bearing Dataset which contains vibration sensor data from industrial bearings.
Dataset includes:
- Vibration signals
- Sensor measurements
- Time series machine condition data
Dataset file used in this repository: