Predictive Agent
LSTM Time-Series Model for Remaining Useful Life
Tech Stack
Python • Scikit-Learn • LSTM • Plotly • Docker • CI/CD
15-20%Interval Extension
LSTMModel
NASAC-MAPSS
Overview
LSTM model extending maintenance intervals 15-20%. Trained on NASA C-MAPSS turbofan dataset.
Problem
Equipment operators need to predict failures before they happen to schedule maintenance proactively and avoid costly unplanned downtime.
Solution
LSTM model trained on NASA C-MAPSS sensor degradation data, predicting Remaining Useful Life from multivariate time-series patterns.
Architecture
Sensor History → Feature Engineering → LSTM Model → RUL Estimation → Maintenance Strategy