LLM of ElectroMechanical World
Deep learning model trained on different capacity inductions, permanent magnets and reluctance motor on various types of load and invertor
In physical system, data generation to train a deep learning model is a complex task. We use a parallel scalable simulator to simulate the elctromechanical system and generate the data considering all the physical effects.
We finetune on the actual motor sensor data. Our trained model can be inferenced through an API for parameter estimation, diagnostic and training edge controller models by just providing basic current voltage sensor data.
Controllers Learning from Simulation Experience
Our Reinforcement Learning based controllers trains in a simulated environment, finds the conditions where it was not optimal and through experimentation learns the optimal solution.
Real time Diagnostics
Machine inspection, parameter tuning and diagnostic can be automated through a simple API and support streaming system for live monitoring.
Reducing human intervention
Predictive diagnosis and performance monitoring prevent breakage and plan schedule maintenance.