Preprocessing-Free Gear Fault Diagnosis Using Small Datasets With Deep Convolutional Neural Network-Based Transfer Learning

By Pei Cao, Shengli Zhang, Jiong Tang. IEEE Access, 2017

372 citations recorded by the source corpus.

Early diagnosis of gear transmission has been a significant challenge, because gear faults occur primarily at microstructure or even material level but their effects can only be observed indirectly at a system level. The performance of a gear fault diagnosis system depends significantly on the features extracted and the classifier subsequently applied. Traditionally, fault-related features are extracted and identified based on domain expertise through data preprocessing which are system-specific and may not be easily generalized. On the other hand, although recently the deep neural networks based approaches featuring adaptive feature extractions and inherent classifications have attracted attention, they usually require a substantial set of training data. Aiming at tackling these issues, this paper presents a deep convolutional neural network-based transfer learning approach. The proposed transfer learning architecture consists of two parts; the first part is constructed with a pre-trained deep neural network that serves to extract the features automatically from the input, and the second part is a fully connected stage to classify the features that needs to be trained using gear fault experimental data. Case analyses using experimental data from a benchmark gear system indicate that the proposed approach not only entertains preprocessing free adaptive feature extractions, but also requires only a small set of training data.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/3c037d7df90e1b588a9a73155bcde21dc47f7d8d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Yihao Xue
2. Ming Zhao
3. Jing Lin
4. Mengjie Huang
5. Xiaolong Liu
6. Yonghua Zheng
7. Rui Yang
8. Qin Wang

In Mechanical Engineering

1. Ming Zhao
2. Jing Lin
3. Xiaolong Liu
4. Yonghua Zheng
5. Qin Wang
6. Olga Fink
7. Markus Svensén
8. Pierre Dersin

In Mechanics of Materials

1. Yihao Xue
2. Ming Zhao
3. Jing Lin
4. Mengjie Huang
5. Rui Yang
6. Zidong Wang
7. Roberto Ferrero
8. Kaixuan Liang

Citations in the corpus

• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “Cao et al. [194] proposed a deep CNN based transfer learning approach for gearbox fault diagnosis.”
• Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks (2020, background): “A number of deep transfer learning approaches have been proposed to improve the scalability and applicability of fault diagnosis methods [23]–[26].”
• Potential, challenges and future directions for deep learning in prognostics and health management applications (2020, background): “These works (Shao et al., 2018; Cao et al., 2018) convert raw signals into graphical images as input, and then fine tune ImageNet pre-trained image classification models for their own fault diagnosis tasks.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, background): “bearings fault diagnosis problems [75], [76], [78], [79], [80].”
• A Digital-Twin-Assisted Fault Diagnosis Using Deep Transfer Learning (2019, methodology): “[35] proposed a deep convolutional neural network-based transfer learning, they used massive image data from ImageNet to pre-train the deep neural network, and then only a small amount of training data is required for gear fault diagnosis.”