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Troubleshooting: a Dynamic Solution for Achieving Reliable Fault Detection by Combining Augmented Reality and Machine Learning

SSRN Electronic Journal
2021
  • 0
    Citations
  • 723
    Usage
  • 10
    Captures
  • 0
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Usage
    723
    • Abstract Views
      607
    • Downloads
      116
  • Captures
    10
  • Ratings
    • Download Rank
      482,372

Article Description

Today’s perplexing maintenance operations and rapid technology development require an understanding of the complex working environment and processing of dynamic and real-time information. However, the environment complexity and an exponential increase in data volume create new challenges and demands and hence make troubleshooting extremely difficult. To overcome the previously mentioned issues and provide the operator real-time access to fast-flowing information, we propose a hybrid solution made of augmented reality further combined with machine learning software. In particular, we present a dynamic reference map of all the required modules and relations that connect machine learning with augmented reality on an example of adaptive fault detection. The proposed dynamic reference map is applied to a pilot case study for immediate validation. To highlight the effectiveness of the proposed solution, the more challenging task of measuring the impact of combining augmented reality with machine learning for fault analysis on maintenance decisions is addressed.

Bibliographic Details

Sara Scheffer; Nick Limmen; Roy Damgrave; Alberto Martinetti; Bojana Rosic; Leo van Dongen

Elsevier BV

Troubleshooting; augmented reality; artificial intelligence; knowledge-based system; maintenance

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