Quantum-behaved particle swarm optimization of convolutional neural network for fault diagnosis

Jie Chen, Qing Shan Xu, Xiaofeng Xue, Yingchao Guo, Runfeng Chen

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

As a deep learning method, Convolutional Neural Network (CNN) can be used in image recognition, fault diagnosis and so on. Due to the internal parameter optimisation problem, the Particle Swarm Optimisation (PSO) has been introduced, but PSO is easy to fall into local optimal solution. In this paper, an adaptive CNN based on Quantum Particle Swarm Optimisation (QPSO-CNN) is proposed and applied to rolling bearings fault diagnosis, which increases the richness of particles and makes it easy to find the global optimal solution. Firstly, the one-dimensional time-series data is compressed by piecewise aggregate approximation algorithm and converted into the heat map by the Gramian angular field algorithm; Secondly, QPSO algorithm is used to search the best CNN model to fit the data set; Finally, the training and validation set are used to search the best network architecture, and the test set is used to test the diagnostic accuracy of the best CNN model, which show that the proposed method has high accuracy.

Original languageEnglish
Pages (from-to)1035-1051
Number of pages17
JournalJournal of Experimental and Theoretical Artificial Intelligence
Volume36
Issue number7
DOIs
StatePublished - 2024

Keywords

  • Convolutional neural network (CNN)
  • fault diagnosis
  • gramian angular field (GAF)
  • particle swarm optimisation (PSO)
  • piecewise aggregate approximation (PAA)
  • quantum particle swarm optimiation (QPSO)

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