Journal Article

A diagnosis method of capsule surface damage based on convolutional neural network

Feng, Shiyuan, Zhang, Zheng, Zhu, Haiping, Hou, Shouwu, Fan, Liangzhi

2022

Overview

In order to accurately identify whether there are damages and damage types on the surface of the aerostat capsule, combined with the powerful data processing capabilities and abnormal pattern recognition capabilities of deep learning, this paper proposes a continuous wavelet transform (CWT) and deep convolution. The diagnosis method of capsule surface damage combined with convolutional neural network (Convolutional Neural Networks, CNN). First, use CWT to convert the collected original stress and strain signals into time-frequency domain images, and then use CNN to classify and identify the time-frequency domain images to determine the damage category of the capsule surface. The CWT-CNN method is different from the traditional fault diagnosis method, it needs to go through the traditional feature extraction process, and the pros and cons of the extracted features often determine the final recognition accuracy. This method effectively overcomes the traditional fault diagnosis method that requires a large amount of signal processing technology. And rich engineering practice experience to extract the shortcomings of fault experience. The experimental results show that the CWT-CNN method can achieve an accuracy of more than 95% in the recognition of the surface damage of the capsule.

• Publisher: EDP Sciences
• Subject: Artificial neural networks / capsule material / Continuous wavelet transform / convolutional neural network / Damage / Data processing / Fault diagnosis / Feature extraction / Frequency domain analysis / Image classification / Machine learning / Neural networks / Pattern recognition / Signal processing / Time-frequency analysis / Wavelet transforms