I’m excited to share a custom IoT Neural Network tool I’ve designed and coded in Python! It connects directly to live IoT sensor data to forecast machine health and predict specific breakdowns up to 2 months in advance. Here is how the dual-network architecture works: The Dual-Network Engine Neural Network 1 (The Forecaster): Analyzes historical IoT data (Vibration, Power, Speed, Heat, etc.) using dynamically optimized LSTM/GRU models to generate a highly accurate 2-month projected “Future Dataset.” Neural Network 2 (The Classifier): Uses Bayesian Optimization to find the ideal LSTM/GRU configuration, then ingests the projected Future Dataset to pinpoint exactly what type of failure is looming on the horizon. Industry Impact & Cost Savings By shifting from reactive to true predictive maintenance, the manufacturing and energy sectors can schedule targeted repairs during planned downtimes. Preventing just one catastrophic breakdown of a critical machine can save large industries millions of dollars annually in lost production, ruined margins, and emergency part replacements. I’ve also custom-built the UI for this software to make the analytics clean and actionable. Check out a preview of the interface and the complete model architecture diagram attached below! https://reddit.com/link/1tqt4kv/video/aa6kipa9s04h1/player submitted by /u/Muda_ahmedi
Originally posted by u/Muda_ahmedi on r/ArtificialInteligence