DOI: 10.5281/zenodo.14860571
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.




Scenario name
Number of work pieces 
Repetitions (screw cylces) per workpiece 
Individual screws per workpiece
Observations
Unique classes
Purpose


S01_thread-degradation
100
25
2
5.000
1
Investigation of thread degradation through repeated fastening


S02_surface-friction
250
25
2
12.500
8
Surface friction effects on screw driving operations


S03_error-collection-1
 
1
2
 
>20
 


S04_error-collection-2
2.500
1
2
5.000
25
 




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 faulty)

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)


2. S02_surface-friction



Focus: Surface friction effects on screw driving operations

Samples: 12,500 screw operations (9,512 normal, 2,988 faulty)

Features: Eight distinct surface conditions (baseline to mechanical damage)

Equipment: Delta PT 40x12 screws, thermoplastic components, surface treatment materials

Process: 25 cycles per location, two locations per workpiece

First published in: CIE51 2024 (West & Deuse, 2024)


Upcoming Datasets:

3. S03_screw-error-collection-1 (recorded but unpublished)



Focus: Varius manipulations of the screw driving process

Features: More than 20 different errors recorded 

First published in: Publication planned

Status: In preparation


4. S04_screw-error-collection-2 (recorded but unpublished)



Focus: Varius manipulations of the screw driving process

Features: 25 distinct errors recorded over the course of a week

First published in: Publication planned

Status: In preparation


5. S05_upper-workpiece-manipulations (recorded but unpublished)



Manipulations of the injection molding process with no changes during tightening


6. S06_lower-workpiece-manipulations (recorded but unpublished)



Manipulations of the injection molding process with no changes during tightening


Additional scenarios may 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 is available in the companion library PyScrew


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 process anomalies

Complete time series data for torque, angle, and additional parameters available

Detailed documentation of experimental conditions and setup

Data collection procedures and equipment specifications available


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

We recommend using our library PyScrew to load and prepare the data. However, the the datasets can be processed using standard JSON and CSV processing libraries. Common data analysis and machine learning frameworks may be used for the analysis. The .tar file provided all information required for each scenario. 

Contact and Support

For questions, issues, or collaboration interests regarding these datasets, either:



Open an issue in our GitHub repository PyScrew

Contact us directly via email 


Acknowledgments

These datasets were collected and prepared by:



RIF Institute for Research and Transfer e.V.

Technical University Dortmund, Institute for Production Systems


The preparation and provision of the research was supported by:



German Ministry of Education and Research (BMBF) 

European Union's "NextGenerationEU" program

The research is part of this funding program

More information regarding the research project is available here


Change Log




Version
Date
Features


v1.1.2
12.02.2025


- Change to default names `label.csv` and `README.md` in all scenarios



v1.1.1
12.02.2025


- Reupload of both s01 and s02 as zip (smaller size) and tar (faster extraction) files

- Change to the data structure (now organized as subdirectories per class in `json/`)



v1.1.0
30.01.2025


- Initial uplload of the second scenario `s02_surface-friction`



v1.0.0
24.01.2025


- Initial upload of the first scenario `s01_thread-degradation`