Generative transfer learning for intelligent fault diagnosis of the wind turbine gearbox

Jianwen Guo, Jiapeng Wu, Shaohui Zhang, Jianyu Long, Weidong Chen, Diego Cabrera, Chuan Li

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

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.

Original languageEnglish
Article number1361
JournalSensors (Switzerland)
Volume20
Issue number5
DOIs
StatePublished - 1 Mar 2020

Bibliographical note

Funding Information:
National Natural Science Foundation of China: 51975121, 51775112, 71801046, Natural Sciences Foundation of Guangdong: 2017A030313690.

Funding Information:
Funding: National Natural Science Foundation of China: 51975121, 51775112, 71801046, Natural Sciences Foundation of Guangdong: 2017A030313690

Publisher Copyright:
© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

Keywords

  • Adversarial training
  • Domain adaptation
  • GAN
  • Wind turbine gearbox

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