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

6 Pages Posted: 20 Oct 2021

See all articles by Sara Scheffer

Sara Scheffer

University of Twente

Nick Limmen

University of Twente

Roy Damgrave

University of Twente

Alberto Martinetti

University of Twente

Bojana Rosic

University of Twente

Leo van Dongen

University of Twente

Date Written: October 19, 2021

Abstract

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.

Keywords: Troubleshooting, augmented reality, artificial intelligence, knowledge-based system, maintenance

Suggested Citation

Scheffer, Sara and Limmen, Nick and Damgrave, Roy and Martinetti, Alberto and Rosic, Bojana and van Dongen, Leo, Troubleshooting: a Dynamic Solution for Achieving Reliable Fault Detection by Combining Augmented Reality and Machine Learning (October 19, 2021). Proceedings of the The 10th International Conference on Through-Life Engineering Services 2021 (TESConf 2021), Available at SSRN: https://ssrn.com/abstract=3945964 or http://dx.doi.org/10.2139/ssrn.3945964

Sara Scheffer (Contact Author)

University of Twente ( email )

Postbus 217
Twente
Netherlands

Nick Limmen

University of Twente ( email )

Postbus 217
Twente
Netherlands

Roy Damgrave

University of Twente ( email )

Postbus 217
Twente
Netherlands

Alberto Martinetti

University of Twente ( email )

Postbus 217
Twente
Netherlands

Bojana Rosic

University of Twente ( email )

Postbus 217
Twente
Netherlands

Leo Van Dongen

University of Twente ( email )

Postbus 217
Twente
Netherlands

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