Location
Hilton Hawaiian Village, Honolulu, Hawaii
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2024 12:00 AM
End Date
6-1-2024 12:00 AM
Description
This paper investigates the application of Machine Learning (ML) approaches for anomaly detection in time series data from screw driving operations, a pivotal process in manufacturing. Leveraging a novel, open-access real-world dataset, we explore the efficacy of several unsupervised and supervised ML models. Among unsupervised models, DBSCAN demonstrates the best performance with an accuracy of 96.68% and a Macro F1 score of 90.70%. Within the supervised models, the Random Forest classifier excels, achieving an accuracy of 99.02% and a Macro F1 score of 98.36%. These results not only underscore the potential of ML in boosting manufacturing quality and efficiency, but also highlight the challenges in their practical deployment. This research encourages further investigation and refinement of ML techniques for industrial anomaly detection, thereby contributing to the advancement of resilient, efficient, and sustainable manufacturing processes. The entire analysis, comprising the complete dataset as well as the Python-based scripts are made publicly available via a dedicated repository. This commitment to open science aims to support the practical application and future adaptation of our work to support business decisions in quality management and the manufacturing industry.
Recommended Citation
West, Nikolai and Deuse, Jochen, "A Comparative Study of Machine Learning Approaches for Anomaly Detection in Industrial Screw Driving Data" (2024). Hawaii International Conference on System Sciences 2024 (HICSS-57). 3.
https://aisel.aisnet.org/hicss-57/da/data_science/3
A Comparative Study of Machine Learning Approaches for Anomaly Detection in Industrial Screw Driving Data
Hilton Hawaiian Village, Honolulu, Hawaii
This paper investigates the application of Machine Learning (ML) approaches for anomaly detection in time series data from screw driving operations, a pivotal process in manufacturing. Leveraging a novel, open-access real-world dataset, we explore the efficacy of several unsupervised and supervised ML models. Among unsupervised models, DBSCAN demonstrates the best performance with an accuracy of 96.68% and a Macro F1 score of 90.70%. Within the supervised models, the Random Forest classifier excels, achieving an accuracy of 99.02% and a Macro F1 score of 98.36%. These results not only underscore the potential of ML in boosting manufacturing quality and efficiency, but also highlight the challenges in their practical deployment. This research encourages further investigation and refinement of ML techniques for industrial anomaly detection, thereby contributing to the advancement of resilient, efficient, and sustainable manufacturing processes. The entire analysis, comprising the complete dataset as well as the Python-based scripts are made publicly available via a dedicated repository. This commitment to open science aims to support the practical application and future adaptation of our work to support business decisions in quality management and the manufacturing industry.
https://aisel.aisnet.org/hicss-57/da/data_science/3