Abstract
Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.
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