Predictive Agent
LSTM Time Series Model for Remaining Useful Life
Tech Stack
Python • Scikit-Learn • LSTM • Plotly • Docker • CI/CD
15 to 20%Interval Extension
LSTMModel
NASAC-MAPSS
Overview
LSTM model extending maintenance intervals 15 to 20 percent. Trained on the 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