112 / 2021-07-30 20:32:23
An Enhanced Intelligent Fault Diagnosis Method to Combat Label Noise
Final Paper
Hulin Ruan / Chongqing university;College of Mechanical and Vehicle Engineering
Yi Wang / Chongqing University;College of Mechanical and Vehicle Engineering; Chongqing University;State Key Laboratory of Mechanical Transmission
Yi Qin / Chongqing university;College of Mechanical and Vehicle Engineering;State Key Laboratory of Mechanical Transmission
Baoping Tang / Chongqing university;State Key Laboratory of Mechanical Transmission
Despite the excellent performance achieved by deep learning-based fault diagnosis approaches, however, the

fault diagnosis under noisy labels is remaining a problem that need to be solved. Due to its complex network structure, the deep learning model can easily fit the noisy labels, which degrades the fault classification performance of deep model. Aiming at the aforementioned issue, this paper proposes a novel noisy label

learning method. Based on Gaussian mixture model, Coteaching technique and semi-supervised learning strategy, the noisy labels are filtered out, refined and reassigned subsequently based on the estimated clean possibility and model prediction. In order to combat the negative effects caused by noisy labels, a more robust training strategy and training goal are presented. The extensive experimental results show the superiority of the proposed method compared with traditional approaches.
Important Date
  • Conference Date

    Oct 21

    2021

    to

    Oct 23

    2021

  • Oct 26 2021

    Registration deadline

Sponsored By
Southeast University, China
Contact Information