摘要
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。
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.
作者
张弛
李浩
胡海涛
朱翀
张玉莹
南国鹏
舒悦
ZHANG Chi;LI Hao;HU Haitao;ZHU Chong;ZHANG Yuying;NAN Guopeng;SHU Yue(Institute of Refrigeration and Cryogenics,Shanghai Jiao Tong University,Shanghai 200240,China;Shanghai Aircraft Design and Research Institute,Commercial Aircraft Corporation of China,Ltd.,Shanghai 201210,China;State Key Laboratory of Compressor Technology(Anhui Laboratory of Compressor Technology),Hefei 230031,Anhui,China)
出处
《化工学报》
EI
CAS
CSCD
北大核心
2021年第S01期430-436,F0004,共8页
CIESC Journal
基金
国家自然科学基金项目(51976115)
上海市科委科技创新行动计划项目(19142203000)
压缩机技术国家重点实验室项目(SKL-YSJ201904)。
关键词
故障诊断
算法
集成
自动特征工程
轴承
模态分解
fault diagnosis
algorithm
integration
automatic feature engineering
bearing
mode decomposition
