Deep learning approach for vibration signals applications

Han Yun Chen, Ching-Hung Lee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations


This study discusses convolutional neural networks (CNNs) for vibration signals analysis, including applications in machining surface roughness estimation, bearing faults diagnosis, and tool wear detection. The one-dimensional CNNs (1DCNN) and two-dimensional CNNs (2DCNN) are applied for regression and classification applications using different types of inputs, e.g., raw signals, and time-frequency spectra images by short time Fourier transform. In the application of regression and the estimation of machining surface roughness, the 1DCNN is utilized and the corresponding CNN structure (hyper parameters) optimization is proposed by using uniform experimental design (UED), neural network, multiple regression, and particle swarm optimization. It demonstrates the effectiveness of the proposed approach to obtain a structure with better performance. In applications of classification, bearing faults and tool wear classification are carried out by vibration signals analysis and CNN. Finally, the experimental results are shown to demonstrate the effectiveness and performance of our approach.

Original languageAmerican English
Article number3929
Issue number11
StatePublished - 7 Jun 2021


  • Convolutional neural network
  • Deep learning
  • Hyper parameter
  • Optimization
  • Short time Fourier transform
  • Vibration signal


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