Troubleshooting: a dynamic solution for achieving reliable fault detection by combining augmented reality and machine learning

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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.
Original languageEnglish
Title of host publication10th International Conference on Through-life Engineering Service
Number of pages6
Publication statusPublished - 16 Nov 2021
Event10th International Conference on Through-life Engineering Service, TESCONF 2021 - Online Event
Duration: 16 Nov 202117 Nov 2021
Conference number: 10

Conference

Conference10th International Conference on Through-life Engineering Service, TESCONF 2021
Abbreviated titleTESCONF 2021
CityOnline Event
Period16/11/2117/11/21

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