Multi-filed data fusion through attention-based networks for readiness prediction in aircraft maintenance: natural language processing (NLP) approach

Military aircraft data is analyzed from a readiness perspective to pursue sustainability. Aircraft readiness can be described as the percentage of fighting force available to perform a mission given a fixed period. It is critical to predict the readiness length of Non-Mission Capable (NMC) to prepare for alternative strategies to achieve mission success before a failure occurs. NMC also affects the maintenance process of an aircraft. In existing readiness state analysis, domain experts must manually assess significant amounts of data and identify the frequency and severity of failure modes, which is time-consuming, subjective, and merely descriptive analytics. This paper proposes a multi-filed data fusion framework through an attention-based network to predict aircraft mission capability. The model employs and investigates structured categorical information and manually inputs textual notes. The attention-based method is applied to retain and identify critical textual details, integrated with the dense representation of various categorical features. We demonstrate that the proposed model framework can contribute to capturing and analyzing essential features related to mission capability. The proposed method's detailed performance is compared with existing approaches.