Aircraft bearing fault diagnosis based on automatic feature engineering

Aiming at the problems that bearing signals in aircraft are simple and mixed with many noises, which require targeted features and high interpretability, a fault diagnosis model composed of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and automatic feature engineering and random forest was developed. Bearing vibration signal is converted into intrinsic mode functions (IMF) through the decomposition method. The core of the model is the feature engineering that automatically performs feature generation and extraction based on 65 kinds of manually designed structural features. This feature engineering can automatically extract the effective features of different objects according to the signal difference of different objects, which has universality between objects. Besides, it can adjust the number of effective features according to different sample sizes, enrich the feature space, and have flexible scalability. Validation shows that the fault classification accuracy of the model based on automatic feature engineering and random forest classification model is 95.32%, which performs better than common model based on singular value entropy, energy entropy, envelope sample entropy feature engineering and support vector machines classification model. Result shows that automatic feature engineering fault diagnosis model can better distinguish different faults on compressor bearings under a large sample size. © 2021, Editorial Board of CIESC Journal. All right reserved.