Industrial screw driving dataset collection: Time series data for process monitoring and anomaly detection

Industrial Screw Driving Datasets Overview This repository contains a collection of real-world industrial screw driving datasets, designed to support research in manufacturing process monitoring, anomaly detection, and quality control. Each dataset represents different aspects and challenges of automated screw driving operations, with a focus on natural process variations and degradation patterns. Dataset Collection The datasets were collected from operational industrial environments, specifically from automated screw driving stations used in manufacturing. Each scenario investigates specific mechanical phenomena that can occur during industrial screw driving operations: Currently Available Datasets: 1. S01_thread-degradation Focus: Investigation of thread degradation through repeated fastening Samples: 5,000 screw operations (4,089 normal, 911 anomalous) Features: Natural degradation patterns, no artificial error induction Equipment: Delta PT 40x12 screws, thermoplastic components Process: 25 cycles per location, two locations per workpiece First published in: HICSS 2024 (West & Deuse, 2024) Upcoming Datasets: 2. S02_surface-friction (recorded and published, dataset publication in progress) Focus: Surface friction effects on screw driving operations Features: Eight manipulation intensities First published in: HICSS 2024 (West & Deuse, 2024) Status: In preparation Additional scenarios will be added to this collection as they become available. Data Format Each dataset follows a standardized structure:- JSON files containing individual screw operation data- CSV files with operation metadata and labels- Comprehensive documentation in README files- Example code for data loading and processing Research Applications These datasets are suitable for various research purposes: Machine learning model development and validation Process monitoring and control systems Quality assurance methodology development Manufacturing analytics research Anomaly detection algorithm benchmarking Usage Notes All datasets include both normal operations and natural process anomalies Complete time series data for torque, angle, and additional parameters Detailed documentation of experimental conditions and setup Data collection procedures and equipment specifications included Access and Citation These datasets are provided under an open-access license to support research and development in manufacturing analytics. When using any of these datasets, please cite the corresponding publication as detailed in each dataset's README file. Related Tools The datasets can be processed using: Standard JSON and CSV processing libraries The companion Python package 'pyscrew' Common data analysis and machine learning frameworks Documentation Each dataset includes: Detailed README files Data format specifications Equipment and process parameters Experimental setup documentation Citation information Contact and Support For questions, issues, or collaboration interests regarding these datasets, please: Open an issue in the respective GitHub repository Contact the authors through the provided institutional channels Acknowledgments These datasets were collected and prepared with support from: German Ministry of Education and Research (BMBF) European Union's "NextGenerationEU" program RIF Institute for Research and Transfer e.V. Technical University Dortmund