Generative Transfer Learning for Intelligent Fault Diagnosis of the Wind Turbine Gearbox

Sensors

Diego Cabrera¹  Weidong Chen²  Jiapeng Wu³  Jianwen Guo³  Chuan Li³  Jianyu Long³  Shaohui Zhang³ 

[1] GIDTEC, Universidad Politécnica Salesiana, Cuenca 010102, Ecuador;Institute of High Energy Physics, CAS, Dongguan 523803, China;School of Mechanical Engineering, Dongguan University of Technology, Dongguan 523808, China;

关键词: domain adaptation;    adversarial training;    gan;    wind turbine gearbox;   

DOI  :  10.3390/s20051361

来源: DOAJ

【 摘 要 】

Intelligent fault diagnosis algorithms based on machine learning and deep learning techniques have been widely used in industrial applications and have obtained much attention as well as achievements. In real industrial applications, working loads of machines are always changing. Hence, directly applying the traditional algorithms will cause significant degradation of performance with changing conditions. In this paper, a novel domain adaptation method, named generative transfer learning (GTL), is proposed to tackle this problem. First, raw datasets were transformed to time−frequency domain based on short-time Fourier transformation. A domain discriminator was then built to distinguish whether the data came from the source or the target domain. A target domain classification model was finally acquired by the feature extractor and the classifier. Experiments were carried out for the fault diagnosis of a wind turbine gearbox. The t-distributed stochastic neighbor embedding technique was used to visualize the output features for checking the effectiveness of the proposed algorithm in feature extraction. The results showed that the proposed GTL could improve classification rates under various working loads. Compared with other domain adaptation algorithms, the proposed method exhibited not only higher accuracy but faster convergence speed as well.

【 授权许可】

Unknown