UR3 CobotOps Clustering Analysis

Overview

This project focuses on the clustering analysis of the UR3 CobotOps dataset. The dataset includes multidimensional time-series data from the UR3 cobot, offering insights into operational parameters and faults for machine learning in robotics and automation.

Dataset

The UR3 CobotOps dataset is a comprehensive collection of data including:

• Electrical currents
• Temperatures
• Speeds across joints (J0-J5)
• Gripper current
• Operation cycle count
• Protective stops
• Grip losses

Dataset Characteristics:

• Type: Multivariate, Time-Series
• Instances: 7409
• Features: 20
• Tasks: Classification, Regression, Clustering

Files in the Repository

1. Dataset: The dataset used for analysis.
2. UR3 CobotOps - UCI Machine Learning Repository.pdf: Documentation explaining the dataset and its variables.
3. Fuzzy Cognitive Maps.pdf: Scientific paper providing theoretical background on Fuzzy Cognitive Maps (FCMs) used in the analysis.
4. UR3 CobotOps Clustering.ipynb: Jupyter notebook with code and visualizations for clustering analysis.

Analysis and Methodology

The project employs clustering techniques to analyze the UR3 CobotOps dataset. The primary methodology involves:

1. Data Pre-processing: Handling missing values, normalizing data, and encoding categorical variables.
2. Clustering: Applying various clustering algorithms to identify patterns and anomalies.
3. Visualization: Creating visualizations to interpret the clustering results.

Key Visualizations

Here are some of the key visualizations from the analysis:

Description: This visualization shows the clustering results of the UR3 CobotOps dataset using K-Means algorithm.

Description: This plot depicts the importance of different features in determining the clusters.

Reproducing the Analysis

To reproduce the analysis, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/ur3-cobotops-clustering.git
   cd UR3-Cobotops-Clustering

2. Install the required dependencies:

   pip install -r requirements.txt

3. Run the Jupyter notebook:

   jupyter notebook UR3 CobotOps Clustering.ipynb

References

• UR3 CobotOps Dataset - UCI Machine Learning Repository
• Tyrovolas, M., Liang, X. S., & Stylios, C. (2023). Information flow-based fuzzy cognitive maps with enhanced interpretability. Granular Computing, 8, 2021-2038. DOI: 10.1007/s41066-023-00417-7

License

This project is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. For more details, see the LICENSE file.

Acknowledgements

This work was supported by the Department of Informatics and Telecommunications, University of Ioannina, and the Industrial Systems Institute, Athena Research Center.