A novel model-independent data augmentation method for fault diagnosis in smart manufacturing

Pin Lyu, Hanbin Zhang, Wenbing Yu, Chao Liu*

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review


With the rapid development of information technology, data-driven fault diagnosis has gained more and more attention because it provides a new way for enterprises to save costs. Considering that there are few abnormalities in equipment operation in actual industrial applications, it is still a challenge to implement data-driven fault diagnosis that requires a large amount of fault data. To tackle the challenge, this paper proposes a model-independent data augmentation method, which is a weighted combination of the two time series data augmentation methods, i.e. Gaussian noise and signal stretching. The experimental dataset is collected from an intelligent motor test platform. The fault diagnosis model based on support vector machine and feedforward neural network are applied to study the ability of the proposed data augmentation method in terms of model independence. Experimental results show that the proposed data augmentation methods can significantly improve the accuracy of fault diagnosis.

Original languageEnglish
Pages (from-to)949-954
Number of pages6
JournalProcedia CIRP
Publication statusPublished - 26 May 2022
Event55th CIRP Conference on Manufacturing Systems, CIRP CMS 2022 - Lugano, Switzerland
Duration: 29 Jun 20221 Jul 2022

Bibliographical note

© 2022 The Authors. CC BY-NC-ND 4.0

Funding Information:
This research work was partially supported by the National Natural Science Foundation of China (No. 52105534) and Shanghai Science and technology program (Project No. 22010500900).


  • data augmentation
  • fault diagnosis
  • smart manufacturing
  • time series


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