Analysis of Aviation Equipment Maintenance Rules Based on Correlation

The maintenance big data management of aviation equipment has always been a major issue in equipment maintenance reform. Especially, the analysis of maintenance errors based on aviation equipment maintenance database is particularly important. This paper proposes an association analysis method based on Apriori algorithm for date mining of a certain aviation equipment maintenance monthly report. Firstly, the BI-LSTM-CRF neural network is used for feature entity recognition. Then, the Apriori algorithm is used for association rule analysis to mine potential laws between multidimensional data, and radar charts are drawn to customize comprehensive evaluation indicators for overall visual analysis of different aviation equipment. Finally, a case demonstration is presented at the end. The proposed method aims to mine the hidden value information in unstructured text materials for key data such as equipment fault location, fault type, and impact degree.