Analysis of High Frequency Data of a Machine Tool via Edge Computing

Stefan Trabesinger; Andre Butzerin; Daniel Schall; Rudolf Pichler

doi:https://doi.org/10.1016/j.promfg.2020.04.028

Analysis of High Frequency Data of a Machine Tool via Edge Computing

Titel in Übersetzung: Analyse von hochfrequenten Daten einer Werkzeugmaschine mittels Edge Computing

Stefan Trabesinger^*, Andre Butzerin, Daniel Schall, Rudolf Pichler

^*Korrespondierende/r Autor/-in für diese Arbeit

Institut für Fertigungstechnik (3010)

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Abstract

New technological capabilities of digitalization are enablers of processing a broad range of machine data. While so-called Low-Frequency Data (LFD) is captured at a sampling rate of several hundred milliseconds, High-Frequency Data (HFD) is based on a sampling rate in the single-digit millisecond range. In this paper, HFD is used to implement an edge-based analytics application for prediction purposes in a machine tool. This edge application leverages Siemens SINUMERIK Edge to capture HFD from a machine tool to recognize anomalies of any kind. The edge application is implemented as a show case in the Learning Factory of Graz University of Technology, the smartfactory@tugraz.

Titel in Übersetzung	Analyse von hochfrequenten Daten einer Werkzeugmaschine mittels Edge Computing
Originalsprache	englisch
Titel	Learning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020
Seiten	343-348
Seitenumfang	6
Band	45
Auflage	2351-9789
DOIs	https://doi.org/10.1016/j.promfg.2020.04.028
Publikationsstatus	Veröffentlicht - 15 Apr. 2020
Veranstaltung	10th Conference on Learning Factories: CLF 2020 - TU Graz, Virtuell, Österreich Dauer: 15 Apr. 2020 → 17 Apr. 2020 https://www.tugraz.at/events/clf2020/home/

Publikationsreihe

Name	Procedia Manufacturing
Herausgeber (Verlag)	Elsevier B.V.

Konferenz

Konferenz	10th Conference on Learning Factories
Kurztitel	CLF 2020
Land/Gebiet	Österreich
Ort	Virtuell
Zeitraum	15/04/20 → 17/04/20
Internetadresse	https://www.tugraz.at/events/clf2020/home/

ASJC Scopus subject areas

Artificial intelligence
Wirtschaftsingenieurwesen und Fertigungstechnik

Fields of Expertise

Mobility & Production

Zugriff auf Dokument

https://doi.org/10.1016/j.promfg.2020.04.028Lizenz: CC BY-NC-ND 4.0

Andere Dateien und Links

SFTUG - smartfactory@tugraz
Pichler, R. & Haas, F.
1/08/17 → 31/07/20
Projekt: Forschungsprojekt

Analysis of High Frequency Data of a Machine Tool via Edge Computing
Stefan Trabesinger (Redner/in), Andre Butzerin (Beitragende/r), Daniel Schall (Beitragende/r) & Rudolf Pichler (Beitragende/r)
17 Apr. 2020
Aktivität: Vortrag oder Präsentation › Vortrag bei Konferenz oder Fachtagung › Science to science

Dieses zitieren

Trabesinger, S., Butzerin, A., Schall, D., & Pichler, R. (2020). Analysis of High Frequency Data of a Machine Tool via Edge Computing. in Learning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020 (2351-9789 Aufl., Band 45, S. 343-348). (Procedia Manufacturing). https://doi.org/10.1016/j.promfg.2020.04.028

Analysis of High Frequency Data of a Machine Tool via Edge Computing. / Trabesinger, Stefan; Butzerin, Andre; Schall, Daniel et al.
Learning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020. Band 45 2351-9789. Aufl. 2020. S. 343-348 (Procedia Manufacturing).

Publikation: Beitrag in Buch/Bericht/Konferenzband › Beitrag in einem Konferenzband › Begutachtung

Trabesinger, S, Butzerin, A, Schall, D & Pichler, R 2020, Analysis of High Frequency Data of a Machine Tool via Edge Computing. in Learning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020. 2351-9789 Aufl., Bd. 45, Procedia Manufacturing, S. 343-348, 10th Conference on Learning Factories, Virtuell, Österreich, 15/04/20. https://doi.org/10.1016/j.promfg.2020.04.028

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title = "Analysis of High Frequency Data of a Machine Tool via Edge Computing",

abstract = "New technological capabilities of digitalization are enablers of processing a broad range of machine data. While so-called Low-Frequency Data (LFD) is captured at a sampling rate of several hundred milliseconds, High-Frequency Data (HFD) is based on a sampling rate in the single-digit millisecond range. In this paper, HFD is used to implement an edge-based analytics application for prediction purposes in a machine tool. This edge application leverages Siemens SINUMERIK Edge to capture HFD from a machine tool to recognize anomalies of any kind. The edge application is implemented as a show case in the Learning Factory of Graz University of Technology, the smartfactory@tugraz.",

keywords = "Anomaly Detection, Edge Computing, High-Frequency Data, Machine Learning, Prediction of Tool Breakage",

author = "Stefan Trabesinger and Andre Butzerin and Daniel Schall and Rudolf Pichler",

year = "2020",

month = apr,

day = "15",

doi = "https://doi.org/10.1016/j.promfg.2020.04.028",

language = "English",

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publisher = "Elsevier B.V.",

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note = "10th Conference on Learning Factories : CLF 2020, CLF 2020 ; Conference date: 15-04-2020 Through 17-04-2020",

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AU - Butzerin, Andre

AU - Schall, Daniel

AU - Pichler, Rudolf

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Y1 - 2020/4/15

N2 - New technological capabilities of digitalization are enablers of processing a broad range of machine data. While so-called Low-Frequency Data (LFD) is captured at a sampling rate of several hundred milliseconds, High-Frequency Data (HFD) is based on a sampling rate in the single-digit millisecond range. In this paper, HFD is used to implement an edge-based analytics application for prediction purposes in a machine tool. This edge application leverages Siemens SINUMERIK Edge to capture HFD from a machine tool to recognize anomalies of any kind. The edge application is implemented as a show case in the Learning Factory of Graz University of Technology, the smartfactory@tugraz.

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KW - Anomaly Detection

KW - Edge Computing

KW - High-Frequency Data

KW - Machine Learning

KW - Prediction of Tool Breakage

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M3 - Conference paper

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T3 - Procedia Manufacturing

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EP - 348

BT - Learning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020

T2 - 10th Conference on Learning Factories

Y2 - 15 April 2020 through 17 April 2020

ER -

Analysis of High Frequency Data of a Machine Tool via Edge Computing

Abstract

Publikationsreihe

Konferenz

ASJC Scopus subject areas

Fields of Expertise

Zugriff auf Dokument

Andere Dateien und Links

Fingerprint

Projekte

SFTUG - smartfactory@tugraz

Aktivitäten

Analysis of High Frequency Data of a Machine Tool via Edge Computing

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