Aarhus Universitets segl

Blazej Tadeusz Leporowski

Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Standard

Detecting Faults during Automatic Screwdriving : A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. / Leporowski, Blazej Tadeusz; Tola, Daniella; Hansen, Casper et al.

Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. red. / Ann-Louise Andersen; Rasmus Andersen; Thomas Ditlev Brunoe; Maria Stoettrup Schioenning Larsen; Kjeld Nielsen; Alessia Napoleone; Stefan Kjeldgaard. Springer, 2022. s. 224-232 (Lecture Notes in Mechanical Engineering).

Publikation: Bidrag til bog/antologi/rapport/proceedingKonferencebidrag i proceedingsForskningpeer review

Harvard

Leporowski, BT, Tola, D, Hansen, C & Iosifidis, A 2022, Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. i A-L Andersen, R Andersen, TD Brunoe, M Stoettrup Schioenning Larsen, K Nielsen, A Napoleone & S Kjeldgaard (red), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. Springer, Lecture Notes in Mechanical Engineering, s. 224-232, 10th World Mass Customization
& Personalization Conference, Aalborg, Danmark, 01/11/2021. https://doi.org/10.1007/978-3-030-90700-6_25

APA

Leporowski, B. T., Tola, D., Hansen, C., & Iosifidis, A. (2022). Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. I A-L. Andersen, R. Andersen, T. D. Brunoe, M. Stoettrup Schioenning Larsen, K. Nielsen, A. Napoleone, & S. Kjeldgaard (red.), Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021 (s. 224-232). Springer. Lecture Notes in Mechanical Engineering https://doi.org/10.1007/978-3-030-90700-6_25

CBE

Leporowski BT, Tola D, Hansen C, Iosifidis A. 2022. Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. Andersen A-L, Andersen R, Brunoe TD, Stoettrup Schioenning Larsen M, Nielsen K, Napoleone A, Kjeldgaard S, red. I Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. Springer. s. 224-232. (Lecture Notes in Mechanical Engineering). https://doi.org/10.1007/978-3-030-90700-6_25

MLA

Leporowski, Blazej Tadeusz et al. "Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving"., Andersen, Ann-Louise , Andersen, Rasmus , Brunoe, Thomas Ditlev Stoettrup Schioenning Larsen, Maria Nielsen, Kjeld Napoleone, Alessia Kjeldgaard, Stefan (red.). Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. Springer. (Lecture Notes in Mechanical Engineering). 2022, 224-232. https://doi.org/10.1007/978-3-030-90700-6_25

Vancouver

Leporowski BT, Tola D, Hansen C, Iosifidis A. Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. I Andersen A-L, Andersen R, Brunoe TD, Stoettrup Schioenning Larsen M, Nielsen K, Napoleone A, Kjeldgaard S, red., Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. Springer. 2022. s. 224-232. (Lecture Notes in Mechanical Engineering). https://doi.org/10.1007/978-3-030-90700-6_25

Author

Leporowski, Blazej Tadeusz ; Tola, Daniella ; Hansen, Casper et al. / Detecting Faults during Automatic Screwdriving : A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving. Towards Sustainable Customization: Bridging Smart Products and Manufacturing Systems - Proceedings of the 8th Changeable, Agile, Reconfigurable and Virtual Production Conference CARV 2021 and 10th World Mass Customization and Personalization Conference MCPC 2021. red. / Ann-Louise Andersen ; Rasmus Andersen ; Thomas Ditlev Brunoe ; Maria Stoettrup Schioenning Larsen ; Kjeld Nielsen ; Alessia Napoleone ; Stefan Kjeldgaard. Springer, 2022. s. 224-232 (Lecture Notes in Mechanical Engineering).

Bibtex

@inproceedings{44ae1393893646e78537a87cce169f3a,
title = "Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving",
abstract = "Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.",
keywords = "Anomaly detection, Automated screwdriving, Fault detection, Time-series dataset, Universal Robots",
author = "Leporowski, {Blazej Tadeusz} and Daniella Tola and Casper Hansen and Alexandros Iosifidis",
year = "2022",
doi = "10.1007/978-3-030-90700-6_25",
language = "English",
isbn = "978-3-030-90699-3",
series = "Lecture Notes in Mechanical Engineering",
publisher = "Springer",
pages = "224--232",
editor = "{ Andersen}, {Ann-Louise } and { Andersen}, {Rasmus } and { Brunoe}, {Thomas Ditlev } and { Stoettrup Schioenning Larsen}, Maria and Nielsen, { Kjeld } and { Napoleone}, {Alessia } and { Kjeldgaard}, { Stefan }",
booktitle = "Towards Sustainable Customization",
address = "Netherlands",
note = "null ; Conference date: 01-11-2021 Through 02-11-2021",
url = "https://carv2020.com/mcpc2020/",

}

RIS

TY - GEN

T1 - Detecting Faults during Automatic Screwdriving

AU - Leporowski, Blazej Tadeusz

AU - Tola, Daniella

AU - Hansen, Casper

AU - Iosifidis, Alexandros

PY - 2022

Y1 - 2022

N2 - Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.

AB - Detecting faults in manufacturing applications can be difficult, especially if each fault model is to be engineered by hand. Data-driven approaches, using Machine Learning (ML) for detecting faults have recently gained increasing interest, where a ML model can be trained on a set of data from a manufacturing process. In this paper, we present a use case of using ML models for detecting faults during automated screwdriving operations, and introduce a new dataset containing fully monitored and registered data from a Universal Robot and OnRobot screwdriver during both normal and anomalous operations. We illustrate, with the use of two time-series ML models, how to detect faults in an automated screwdriving application.

KW - Anomaly detection

KW - Automated screwdriving

KW - Fault detection

KW - Time-series dataset

KW - Universal Robots

U2 - 10.1007/978-3-030-90700-6_25

DO - 10.1007/978-3-030-90700-6_25

M3 - Article in proceedings

SN - 978-3-030-90699-3

T3 - Lecture Notes in Mechanical Engineering

SP - 224

EP - 232

BT - Towards Sustainable Customization

A2 - Andersen, Ann-Louise

A2 - Andersen, Rasmus

A2 - Brunoe, Thomas Ditlev

A2 - Stoettrup Schioenning Larsen, Maria

A2 - Nielsen, Kjeld

A2 - Napoleone, Alessia

A2 - Kjeldgaard, Stefan

PB - Springer

Y2 - 1 November 2021 through 2 November 2021

ER -