DOI: 10.5281/zenodo.14729548
title: Industrial screw driving dataset collection: Time series data for process monitoring and anomaly detection

description: 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