An incorrect or incomplete repair card, typically used in aviation maintenance for reporting failures, may result in incorrect maintenance and make it very hard to analyse the maintenance data. There are several reasons for this incomplete reporting. Firstly, (part of) the information is often unknown at the moment the maintenance crew fills in the card. Also, the findings on repair cards are generally filled out as freeform text, making it difficult to automatically interpret the findings. An automatically assessed failure description will lead to more complete and consistent repair cards. This will
also improve the efficiency of troubleshooting since this failure diagnosis can add information which would otherwise not be at the disposal of the maintenance crew at that time. This research will utilise a data driven approach combining maintenance and usage data. The model will be based on Artificial Intelligence (AI) such that it is no longer necessary to completely understand the physics of a (sub)system or component. XAI (eXplainable AI) will be added to the model to provide transparency and interpretability of the assessed diagnosis. The different steps towards this failure diagnosing
model are applied to a case study with a main wheel of the RNLAF (Royal Netherlands Air Force) F-16. This preliminary feasibility study already showed the value of this automated failure diagnosis model with an improvement in diagnosis accuracy from 60% to 69%
How to Cite
aviation maintenance, explainable artificial intelligence, data driven, automated failure diagnosis, AI
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