Monitoring and analyzing temperature variations in electric motors is essential for optimizing performance and ensuring operational efficiency. This project presents a Python-based dashboard built with LightningChart Python, enabling the visualization of key motor parameters, including temperature, torque, and motor speed.

LightningChart Python is a high-performance visualization library that enables developers to create advanced visualizations with optimized rendering. It is particularly useful for working with large datasets, making it well-suited for this electric motor analysis project.

Key Features of LightningChart Python

• Optimized rendering for handling large datasets.
• Interactive charts with dynamic customization.
• Comprehensive support for multiple chart types including line charts, scatter plots, histograms, and heatmaps.

To set up the Python environment for this Python flight tracking application project, you need to install Python and the necessary libraries such as LightningChart Python. You can check the original project on GitHub.

Here’s a quick setup guide:

1. Install Python: Download and install the latest version of Python from the official website.
2. Install Libraries: Use pip to install the required libraries:

3. Set up your development environment by creating a new project directory and installing the required libraries. Ensure that LightningChart Python is properly licensed.

Dataset Extraction

The dataset is provided in a compressed .7z file to reduce repository size. Before running the code, please extract the dataset found inside the dataset folder.

Overview of Libraries Used

• Pandas: For data manipulation and analysis. (documentation)
• NumPy: For numerical computations, particularly in data binning and transformations. (documentation)
• LightningChart Python: For creating high-performance, interactive data visualizations. (documentation)

The core of this project is the Electric Motor Temperature Dashboard, which consists of multiple visualizations:

This line chart visualizes the average temperature of different components over multiple measurement sessions (profile IDs). The temperatures analyzed include coolant, stator winding, stator tooth, stator yoke, permanent magnet (pm), and ambient temperature.

A histogram is used to analyze the distribution of stator winding temperature, helping identify its range and frequency.

This scatter plot provides insights into the relationship between torque and motor speed, with color gradients representing variations in torque values.

This heatmap represents the average stator winding temperature as a function of motor speed and torque. Higher temperatures are visually distinguished through a color gradient.

Bringing all visualizations together, the final dashboard provides an interactive and comprehensive analysis of electric motor behavior.

1. Top Left: Line chart showing average temperature trends.
2. Right: Histogram of stator winding temperature distribution.
3. Middle: Scatter plot for torque vs. motor speed.
4. Bottom: Heatmap displaying temperature variations across motor speed and torque.

This project demonstrates the effectiveness of LightningChart Python in creating an advanced Electric Motor Temperature Dashboard. With optimized rendering and interactive visualizations, this dashboard enables detailed motor analysis, improving insights into temperature behavior, torque, and performance characteristics.

The techniques applied in this project can be extended to real-world industrial applications for monitoring and predictive maintenance of electric motors.