🔥 Electric Motor Temperature Prediction (PMSM)

This project predicts the rotor temperature of a Permanent Magnet Synchronous Motor (PMSM) using machine learning techniques. It uses various operational parameters (like voltage, current, speed, etc.) as input and outputs the predicted rotor temperature.

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📌 Project Objective

Permanent Magnet Synchronous Machines (PMSMs) are widely used in industrial, automotive, and commercial systems due to their efficiency and reliability. Predicting rotor temperature helps in ensuring motor safety, performance, and extending operational life.

This ML-based Flask app allows users to input real-time motor parameters and get a predicted rotor temperature instantly.

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💡 Key Features

• Predict rotor temperature based on real motor parameters
• Trained using models like Linear Regression, Decision Tree, Random Forest, SVM
• Selected best-performing model saved as best_model.pkl
• Integrated with a modern Flask-based frontend UI
• Input validation and fallback prediction for safety
• Deployed locally, extendable for edge device integration

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🛠 Tech Stack & Requirements

• Python 3.10+
• Flask 3.1.1
• Scikit-learn 1.6.1
• Pandas 2.2.2
• NumPy 2.0.2
• Joblib for model serialization

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📦 Installed Packages

Package	Version
Flask	3.1.1
pandas	2.2.2
numpy	2.0.2
scikit-learn	1.6.1
scipy	1.16.0
joblib	1.5.1
Jinja2	3.1.6
Werkzeug	3.1.3
blinker	1.9.0
python-dateutil	2.9.0.post0
pytz	2025.2
tzdata	2025.2
others	click, colorama, MarkupSafe, etc.

🚀 How to Run Locally

1. Clone the Repository

git clone https://github.com/BHARTIYAYASH/electric-motor-temp-vit.git
cd motor-temp-prediction

2. Create a Virtual Environment and Activate It

# Create virtual environment
python -m venv env

# Activate on Windows
env\Scripts\activate

# Activate on macOS/Linux
source env/bin/activate

3. Install All Required Packages

pip install -r requirements.txt

4. Ensure the Model File Exists

Make sure the file best_model.pkl (your trained ML model) is present in the project root directory.

5. Run the Flask App

python app.py

6. Access the App in Your Browser

Visit: http://127.0.0.1:5000

🧪 Sample Inputs for Prediction

You can use the following values to test the prediction:

Feature	Sample Value	Description
u_q	50.0	Q-axis voltage (V)
coolant	35.0	Coolant temperature (°C)
stator_winding	60.0	Stator winding temperature (°C)
u_d	48.0	D-axis voltage (V)
stator_tooth	55.0	Stator tooth temperature (°C)
motor_speed	1475.0	Motor speed (RPM)
i_d	1.2	D-axis current (A)
i_q	0.8	Q-axis current (A)
stator_yoke	52.0	Stator yoke temperature (°C)
torque	42.0	Motor torque (Nm)

✅ Sample Output

Predicted Rotor Temperature: 74.38°C