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

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paper

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.

Translated title of the contributionAnalyse von hochfrequenten Daten einer Werkzeugmaschine mittels Edge Computing
Original languageEnglish
Title of host publicationLearning Factories across the value chain – from innovation to service – The 10th Conference on Learning Factories 2020
Pages343-348
Number of pages6
Volume45
Edition2351-9789
DOIs
Publication statusPublished - 15 Apr 2020
Event10th Conference on Learning Factories: CLF 2020 - TU Graz, Virtuell, Austria
Duration: 15 Apr 202017 Apr 2020
https://www.tugraz.at/events/clf2020/home/

Publication series

NameProcedia Manufacturing
PublisherElsevier B.V.

Conference

Conference10th Conference on Learning Factories
Abbreviated titleCLF 2020
CountryAustria
CityVirtuell
Period15/04/2017/04/20
Internet address

Keywords

  • Anomaly Detection
  • Edge Computing
  • High-Frequency Data
  • Machine Learning
  • Prediction of Tool Breakage

ASJC Scopus subject areas

  • Artificial Intelligence
  • Industrial and Manufacturing Engineering

Fields of Expertise

  • Mobility & Production

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