Digital Twin of an Induction Motor: Fault Analysis and Predictive Maintenance
This repositiory contains my Digital Twin Induction Motor Experimnet. The details for this experiment are given below.
In this model, we basically present an efficient method to predict the fault in motors using digital twin and predictive machine learning models to estimate the fault in induction motor. The faults in an induction motor can be broken down into three different categories which are bearing faults, stator faults and unbalanced voltages and centricity. These faults have direct impact on two important parameters which are vibrational signals and stator currents, and these two parameters will be used in our predictive machine learning model for further analysis. All the other parameters are physical in nature and cannot be modelled using simulated motors. This includes using detection models on sensory thermography, oil analysis, and ultrasound data.
The project is structured as follows:
Digital-twin
├── data/ # data directory
├── main/ # main file
├── presentation/ # presentation file
├── report/ # report file
├── LICENSE # license file
├── README.md # readme fileTo get started with downloading this repository, follow the steps below:
Clone the repository to your local machine using the following command:
https://github.com/ahmd-mohsin/Digital-Twin-Induction-motor.git
For any further recommnded changes and collaborations, feel free to contact. With ❤️ Ahmed.