Rolling bearing fault diagnosis method based on Gramian angle field and PSO CNN

DOI:	10.11809/bqzbgcxb2024.04.039
Keywords:	Gramian angle field; particle swarm optimization algorithm; convolutional neural networks; rolling bearing; fault diagnosis
Abstract:	The structure of convolutional neural network has a great influence on the fault diagnosis accuracy of rolling bearings, a fault diagnosis method based on Gramian angular field and particle swarm optimization of convolutional neural network structure is proposed. The one dimensional bearing vibration data is reconstructed using the Gramian angular field, retaining the original data information while including time correlation; the particle swarm optimization algorithm is used to iteratively optimize the encoded convolutional neural network structure. The method is experimentally validated using thebearing dataset from Case Western Reserve University. Experimental results show that the proposed method can generate network structure adaptively, and the average diagnostic accuracy is 99%, which can obtain better fault diagnosis accuracy than other mainstream convolutional neural network structures.
Published:	2024-04-30
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