A deep neural network-based feature fusion for bearing fault diagnosis

Duy Tang Hoang , Xuan Toa Tran, Mien Van, Hee Jun Kang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

96 Downloads (Pure)


This paper presents a novel method for fusing information from multiple sensor systems for bearing fault diagnosis. In the proposed method, a convolutional neural network is exploited to handle multiple signal sources simultaneously. The most important finding of this paper is that a deep neural network with wide structure can extract automatically and efficiently discriminant features from multiple sensor signals simultaneously. The feature fusion process is integrated into the deep neural network as a layer of that network. Compared to single sensor cases and other fusion techniques, the proposed method achieves superior performance in experiments with actual bearing data.
Original languageEnglish
Article number244
Number of pages13
JournalSensors (Basel, Switzerland)
Issue number1
Publication statusPublished - 01 Jan 2021


Dive into the research topics of 'A deep neural network-based feature fusion for bearing fault diagnosis'. Together they form a unique fingerprint.

Cite this