Whitening CNN-Based Rotor System Fault Diagnosis Model Features

Jesse Miettinen*, Riku-Pekka Nikula, Joni Keski-Rahkonen, Fredrik Fagerholm, Tuomas Tiainen, Seppo Sierla, Raine Viitala

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Intelligent fault diagnosis (IFD) models have the potential to increase the level of automation and the diagnosis accuracy of machine condition monitoring systems. Many of the latest IFD models rely on convolutional layers for feature extraction from vibration data. The majority of these models employ batch normalisation (BN) for centring and scaling the input for each neuron. This study includes a novel examination of a competitive approach for layer input normalisation in the scope of fault diagnosis. Network deconvolution (ND) is a technique that further decorrelates the layer inputs reducing redundancy among the learned features. Both normalisation techniques are implemented on three common 1D-CNN-based fault diagnosis models. The models with ND mostly outperform the baseline models with BN in three experiments concerning fault datasets from two different rotor systems. Furthermore, the models with ND significantly outperform the baseline models with BN in the common CWRU bearing fault tests with load domain shifts, if the data from drive-end and fan-end sensors are employed. The results show that whitened features can improve the performance of CNN-based fault diagnosis models.

Original languageEnglish
Article number4411
Number of pages22
JournalAPPLIED SCIENCES
Volume12
Issue number9
DOIs
Publication statusPublished - 1 May 2022
MoE publication typeA1 Journal article-refereed

Keywords

  • CNN architecture
  • normalization techniques
  • intelligent fault diagnosis
  • vibration
  • CONVOLUTIONAL NEURAL-NETWORK
  • DEEP LEARNING-MODEL
  • MACHINE

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