基于自动特征工程的飞行器轴承故障诊断

针对飞行器轴承信号单一且噪声多、需要针对性特征以及需要高可解释性的问题，开发了涵盖具有自适应噪声的完全集合经验模态分解（CEEMDAN）、自动特征工程以及随机森林的故障诊断模型，模型核心为自动进行特征生成以及提取的特征工程。通过该特征工程能够根据不同对象的信号差异，自动提取出不同对象的有效特征，具备对象间的通用性，且该特征工程可根据样本量的不同调整有效特征的数量，丰富特征空间，具备灵活的可扩展性。验证表明，该涵盖自动特征工程的模型的故障分类准确率为95.32%，可较好地在大样本量下区分压缩机轴承上的不同故障。

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.