A hybrid feature selection model for identifying groups of critical elements in aero‐engine assembly

Abstract

Aero‐engine assembly has a wide range of indicators, and finding critical assembly elements to guide assembly and troubleshooting is essential in improving the reliability and safety of aero‐engine. In order to identify critical elements in aero‐engine assembly components, this study aims to establish a two‐stage hybrid feature selection model, namely, FSBP approach, which integrated filter method and particle swarm algorithm with Bayesian optimization. Specifically, individual filter feature selection methods are compared to select a relatively effective method to reduce the data dimensions and ensure the quality of the initial subset. Then, the particle swarm algorithm combined with Bayesian optimization obtains a subset of features in the second stage that are more suitable for predictive models with more robust classification prediction capability. The algorithm is successfully applied to real aero‐engine assembly and trial test datasets, and the experimental results show that our proposed two‐stage hybrid feature selection model outperforms other benchmark methods.