Predictive Maintenance

Equipment Failure Prediction with LSTM

LSTMNASA DatasetFFT Analysis
92%F1 Score
48hrsEarly Warning
30%Cost Reduction
Hugging FaceGitHubBack

Problem

Unplanned equipment failures cause costly downtime and safety hazards. Traditional scheduled maintenance is wasteful and ineffective.

Solution

LSTM neural network trained on NASA turbofan dataset. Uses FFT for vibration analysis and predicts Remaining Useful Life (RUL) with 48-hour lead time.

Architecture

Sensor Data → FFT Processing → LSTM Model → FastAPI Endpoint → RUL Prediction → Alert System with Azure IoT Hub integration.

Predictive Maintenance Demo

Vibration Sensor DataNORMAL
42
00:0004:0008:0012:0016:0020:0024:00
168HOURS
Remaining Useful Life

LSTM Prediction Model

Real Time vibration analysis using Fast Fourier Transform (FFT) and LSTM neural networks trained on NASA turbofan dataset.

92%F1 Score
48hrsLead Time