Applications of convolutional neural networks in fault diagnosis of mechanical-electrical-hydraulic systems: a review

Zenggaung Liu; Yan Zhou; Daling Yue; Benguo Zhang; Ke Feng; Shuaidi Zhang

doi:10.1117/12.3026211

1 April 2024 Applications of convolutional neural networks in fault diagnosis of mechanical-electrical-hydraulic systems: a review

Zenggaung Liu, Yan Zhou, Daling Yue, Benguo Zhang, Ke Feng, Shuaidi Zhang

Author Affiliations +

Proceedings Volume 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023); 130820N (2024) https://doi.org/10.1117/12.3026211
Event: 2023 4th International Conference on Mechanical Engineering, Intelligent Manufacturing and Automation Technology (MEMAT 2023), 2023, Guilin, China

Abstract

The mechanical-electrical-hydraulic systems are developing towards automation and intelligence, which raise higher requirements for accurate and smart fault diagnosis of the core component in these systems. Traditional diagnostic methods not only rely too much on a priori knowledge for feature extraction but also have limited computational ability for massive data, which can’t satisfy the demand for intelligent fault diagnosis. Thanks to the deep network structure, deep learning (DL) can automatically extract the deep implicit feature information from multiple data without a priori knowledge and has an immense advantage in realizing complicated fault diagnosis. As a typical representative of deep learning, Convolutional neural network (CNN) can directly process images and has made significant achievements in image recognition and classification, therefore scholars have conducted extensive research on CNN for fault diagnosis. Based on the above-mentioned content, this review sorts out the current research works employing the 1DCNN model, 2DCNN model and hybrid model of CNN for fault diagnosis in mechanical-electrical-hydraulic systems, and then summarizes the advantages and disadvantages of the three CNN models, respectively. Finally, we discuss the future development directions and challenges of fault diagnosis with CNN.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zenggaung Liu, Yan Zhou, Daling Yue, Benguo Zhang, Ke Feng, and Shuaidi Zhang "Applications of convolutional neural networks in fault diagnosis of mechanical-electrical-hydraulic systems: a review", Proc. SPIE 13082, Fourth International Conference on Mechanical Engineering, Intelligent Manufacturing, and Automation Technology (MEMAT 2023), 130820N (1 April 2024); https://doi.org/10.1117/12.3026211

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KEYWORDS

Diagnostics

Feature extraction

Data modeling

Machine learning

Vibration

RGB color model

Signal processing

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