WDA : An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

4 Scopus Citations
View graph of relations

Author(s)

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number4394
Journal / PublicationSensors
Volume21
Issue number13
Online published26 Jun 2021
Publication statusPublished - Jul 2021

Link(s)

Abstract

With the growth of computing power, deep learning methods have recently been widely used in machine fault diagnosis. In order to realize highly efficient diagnosis accuracy, people need to know the detailed health condition of collected signals from equipment. However, in the actual situation, it is costly and time-consuming to close down machines and inspect components. This seriously impedes the practical application of data-driven diagnosis. In comparison, the full-labeled machine signals from test rigs or online datasets can be achieved easily, which is helpful for the diagnosis of real equipment. Thus, we introduced an improved Wasserstein distance-based transfer learning method (WDA), which learns transferable features between labeled and unlabeled signals from different forms of equipment. In WDA, Wasserstein distance with cosine similarity is applied to narrow the gap between signals collected from different machines. Meanwhile, we use the Kuhn– Munkres algorithm to calculate the Wasserstein distance. In order to further verify the proposed method, we developed a set of case studies, including two different mechanical parts, five transfer scenarios, and eight transfer learning fault diagnosis experiments. WDA reached an average accuracy of 93.72% in bearing fault diagnosis and 84.84% in ball screw fault diagnosis, which greatly surpasses state-of-the-art transfer learning fault diagnosis methods. In addition, comprehensive analysis and feature visualization are also presented.

Research Area(s)

  • Convolutional neural network, Domain adaptive ability, Intelligent bearing fault diagnosis, Kuhn–Munkres algorithm, Wasserstein distance

Citation Format(s)

WDA : An Improved Wasserstein Distance-Based Transfer Learning Fault Diagnosis Method. / Zhu, Zhiyu; Wang, Lanzhi; Peng, Gaoliang; Li, Sijue.

In: Sensors, Vol. 21, No. 13, 4394, 07.2021.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

Download Statistics

No data available