Evaluation of the Potential of Deep Learning for Manufacturing Process Analytics

Elias Hagendorfer

Research output: ThesisMaster's Thesis

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Abstract

This thesis investigates the use of deep learning for the automatic identification of machine operations from multivariate time-series data emanating from sensors and actuators. Methods from deep learning and time-series analysis are reviewed with the aim of determining their suitability. A new approach is introduced to alleviate weaknesses in current approaches which include insufficient signal selection, requirement of large amount of training data or neglection of the physical nature of the system. It consists of: a preprocessing methodology based around stationarity tests, redundancy analysis and entropy measures; a deep learning algorithm classifying time series segments into operation categories; a process analytics framework dealing with operation length and frequency. The approach was applied successfully to several datasets from heavy machinery bulk handling systems.
Translated title of the contributionBewertung des Potentials von Deep Learning zur Prozessanalytik in der Produktion
Original languageEnglish
QualificationDipl.-Ing.
Awarding Institution
  • Montanuniversität
Supervisors/Advisors
  • O'Leary, Paul, Supervisor (internal)
Award date23 Mar 2018
Publication statusPublished - 2018

Bibliographical note

no embargo

Keywords

  • deep learning
  • time-series analysis
  • signal selection
  • manufacturing process
  • sensor data

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