Status Recognition of Magnetic Fluid Seal Based on High-Order Cumulant Image and VGG16

Abstract

A magnetic fluid seal is often used in complex working conditions with harsh environmental requirements. Timely and accurate identification of the seal status can help avoid the major economic losses and even casualties caused by the seal failure. However, research on the recognition of magnetic fluid seal status is still at the exploratory stage internationally. Aiming at the problem of inclusion of other components and Gaussian noise when using acoustic emission nondestructive testing technology to detect the magnetic fluid seal status, a new recognition method based on the combination of high-order cumulant image and VGG16 convolutional neural network is proposed to identify the magnetic fluid seal status in this paper. In this method, high-order cumulant images are used for the denoising and feature selecting of detected signals, and the VGG16 convolutional neural network is trained to automatically learn image features to classify and recognize high-order cumulant images representing different sealing states. Experiments show that the accuracy of image recognition using VGG16 is significantly higher than that of other methods. The VGG16 method can identify the magnetic fluid seal state accurately and effectively, with strong robustness and Gaussian noise suppression ability.