A reinforcement neural architecture search convolutional neural network for rolling bearing fault diagnosis

Lintao Li, Hongkai Jiang, Ruixin Wang, Qiao Yang

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

The complexity of machinery makes accurate identification of rolling bearing fault signals difficult. Convolutional neural networks (CNNs) have made some progress, but they rely on the expertise of the network designer and the iterative process of optimizing numerous parameters. Therefore, there is an urgent need to develop a method that reduces the threshold for designing CNNs for a given task. In this article, we propose a reinforcement neural architecture search CNN to address this problem. Firstly, we design a neural architecture search algorithm that can generate different types of sub-networks specifically for fault diagnosis tasks. Secondly, we execute a reinforcement learning-based search strategy to discover promising sub-networks. Furthermore, we enhance the performance of the sub-network by improving the optimizer and training parameters. We conduct extensive experiments using two different types of datasets and verify that the proposed method’s fault classification capability is superior to existing methods.

Original languageEnglish
Article number115122
JournalMeasurement Science and Technology
Volume34
Issue number11
DOIs
StatePublished - Nov 2023

Keywords

  • fault diagnosis
  • neural architecture search
  • optimization algorithm
  • reinforcement learning
  • rolling bearing

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