Troubleshooting: a Dynamic Solution for Achieving Reliable Fault Detection by Combining Augmented Reality and Machine Learning
SSRN Electronic Journal
2021
- 723Usage
- 10Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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.
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