Torque Measurement Visualization and Analysis Application

This application is designed to assist in visualizing, analyzing, and evaluating torque measurements from your in-house automatic torque measurement machine. It provides a user-friendly interface to process CSV data files, perform advanced analyses, and determine PASS or FAILED results based on multiple criteria.

Table of Contents

• Features
• Getting Started
  • Prerequisites
  • Installation
• Usage
  • Running the Application
  • Using the Interface
• Analysis Criteria
  • 1. Sudden Spike Detection
  • 2. HPF_RMS Threshold Comparison
  • 3. Machine Learning Prediction
• Project Structure
• Future Work
• Contributing
• License

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Features

• Simultaneous Visualization: Display all graphs for up to two measurement files side by side for easy comparison.
• Adjustable Parameters:
  • High-pass filter cutoff frequency.
  • RMS window size for moving RMS calculation.
  • HPF_RMS threshold value.
  • Y-axis scale adjustment.
• Automatic File Detection: The application automatically detects new CSV files added to the specified directory without needing to refresh the page.
• PASS or FAILED Analysis:
  • Detect sudden spikes in filtered data.
  • Compare HPF_RMS values against a user-defined threshold.
  • Placeholder for machine learning model predictions.
• Data Download: Easily download processed CSV and PDF reports.
• User-Friendly Interface: Intuitive layout with helpful tooltips and documentation.

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Getting Started

Prerequisites

• Python 3.7 or higher
• Git (optional, for cloning the repository)
• specify your data directory.

Installation

1. Clone the Repository:

   git clone https://github.com/plypieng/InteractiveTorqueProject.git

2. Navigate to the Project Directory:

   cd InteractiveTorqueProject

3. Create a Virtual Environment (recommended):

   python -m venv venv

4. Activate the Virtual Environment:

   • On Windows:

     venv\Scripts\activate

   • On Unix or MacOS:

     source venv/bin/activate

5. Install Dependencies:

   pip install -r requirements.txt

6. Configure the Data Directory:

   • Ensure that your CSV data files are located in the W:/ drive.

   • If you need to change the data directory, update the ALLOWED_DIRECTORY variable in app.py:

     ALLOWED_DIRECTORY = 'path/to/your/data/directory'

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Usage

Running the Application

1. Start the Application:

   python app.py

2. Access the Application:

   • Open a web browser and navigate to http://localhost:8050.

Using the Interface

1. Select Measurement Files:

   • Choose up to two CSV files from the dropdown menus to visualize and compare their data.

2. Adjust Parameters:

   • High-Pass Filter Cutoff Frequency: Enter the desired cutoff frequency in Hz.
   • RMS Window Size: Specify the window size for calculating the moving RMS.
   • HPF_RMS Threshold: Set the threshold value for HPF_RMS analysis.
   • Y-axis Scale: Use the slider to adjust the Y-axis range of the plots.

3. View Graphs and Analysis:

   • The application displays the normal data plot, filtered data plot, and FFT plot for each selected file.
   • Extracted features and analysis results are shown alongside the graphs.

4. Interpret Analysis Results:

   • Overall Result: Displays PASS or FAILED based on the analysis criteria.
   • Detailed Messages: Provides information on sudden spike detection, HPF_RMS threshold comparison, and machine learning predictions.

5. Download Data:

   • Use the "Download CSV" and "Download PDF" buttons to download the processed data and reports.

6. Automatic File Updates:

   • The application automatically updates the file selection options when new CSV files are added to the data directory.

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Analysis Criteria

1. Sudden Spike Detection

• Purpose: Identify sudden spikes in the filtered data, which may indicate dust particles or surface defects on the ball screw.
• Method:
  • Calculate the absolute difference between consecutive points in the high-pass filtered data.
  • Detect spikes where the difference exceeds a predefined threshold.
• Result:
  • If spikes are detected, the analysis recommends re-measurement.

2. HPF_RMS Threshold Comparison

• Purpose: Assess the roughness of the ball screw surface based on the HPF_RMS value.
• Method:
  • Calculate the moving RMS of the filtered data using the specified window size.
  • Compare the maximum HPF_RMS value against the user-defined threshold.
• Result:
  • If the HPF_RMS exceeds the threshold, it may indicate excessive surface roughness, leading to increased heat generation.

3. Machine Learning Prediction

• Purpose: Utilize advanced machine learning models to predict the condition of the ball screw.
• Method:
  • Placeholder: Currently, this is a placeholder function.
  • Future Integration: Plans to incorporate models like LSTM autoencoders or supervised learning models trained on the CSV data.
• Result:
  • Provides a PASS or FAILED prediction based on the model's output.

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Project Structure

torque-measurement-app/
├── app.py                # Main Dash application
├── utils.py              # Utility functions
├── plots.py              # Plot creation functions
├── pdf_generator.py      # PDF report generation
├── requirements.txt      # Python dependencies
├── README.md             # Project documentation

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Future Work

• Machine Learning Integration:

  • Train and integrate machine learning models for more accurate predictions.
  • Models may include LSTM autoencoders, supervised classifiers, etc.

• Enhanced Reporting:

  • Improve PDF report generation to include analysis results and recommendations.

• User Interface Improvements:

  • Add notifications or alerts for new files detected.
  • Provide more customization options for plots and analysis parameters.

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License

This project is licensed under the MIT License. You are free to use, modify, and distribute this software as per the terms of the license.

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