patelharsh21/stator-motor-deploy-streamlit

Overview

The AIFD MotorFault GUI is a graphical user interface designed to aid in the intelligent diagnosis of stator faults in induction motors. This project leverages vibration signals and machine learning to provide an end-to-end solution for motor fault detection and severity prediction. The GUI allows users to upload motor data and receive detailed fault diagnosis results without requiring any coding knowledge. App link: AIFD MotorFault GUI Paper link : Research Paper

Features

• User-Friendly Interface: Built with Streamlit, the GUI enables easy dataset uploads, exploratory data analysis (EDA) visualization, and interaction with prediction results.
• Automated Model Selection: Utilizes AutoKeras to automatically select and optimize the best deep learning model for the given dataset.
• Explainable AI: Incorporates Explainable AI techniques to improve model transparency and trustworthiness.
• High Accuracy: Achieves 99.81% accuracy in stator fault detection and severity prediction.

Requirements

• Python 3.10

Installation

1. Clone the repository:

   git clone [email protected]:patelharsh21/stator-motor-deploy-streamlit.git

2. Install the required dependencies:

   pip install -r requirements.txt

Usage

1. Run the Streamlit application:

   streamlit run app.py

2. Open your web browser and go to http://localhost:8501.

3. Upload your motor vibration data through the provided interface. A sample vibration data file is provided in the repo.

4. View the EDA results and interact with the fault diagnosis predictions.

Block Diagram

Methodology

1. Data Collection: Vibration signals from the stator motor are collected using a Brüel & Kjær (B&K) accelerometer.
2. Preprocessing: The data undergoes preprocessing, including invalid sample removal and normalization.
3. Feature Extraction: Various time-domain and frequency-domain features are extracted from the vibration data.
4. Model Training: An AutoKeras model is trained on the processed data to identify the optimal model for fault diagnosis.
5. Prediction and Explanation: The trained model predicts stator faults and their severity, with Explainable AI providing insights into the model's decision-making process.

Results

UI

Results

XAI (Explainable AI)

The proposed method demonstrates high accuracy in detecting and predicting the severity of stator faults in induction motors. The integration of a user-friendly GUI and Explainable AI techniques ensures that the solution is both accessible and trustworthy for end-users in industrial applications.

Contributors

• Jitendra Kumar Dewangan (IIIT Naya Raipur)
• Harsh Patel (IIIT Naya Raipur)
• Aparna Sinha (IIIT Naya Raipur)
• Debanjan Das (IIT Kharagpur)