Online Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

By Gaowei Xu, Min Liu, Zhuofu Jiang, Weiming Shen, Chenxi Huang. IEEE Transactions on Instrumentation and Measurement, 2020

298 citations recorded by the source corpus.

Fault detection and diagnosis (FDD) is crucial for stable, reliable, and safe operation of industrial equipment. In recent years, deep learning models have been widely used in data-driven FDD methods because of their automatic feature learning capability. In general, these models are trained on historical sensor data, and therefore, it is very difficult to meet the real-time requirement of online FDD applications. Since transfer learning can solve different but similar problems in the target domain efficiently and effectively with the knowledge learned from the source domain, this paper proposes an online fault diagnosis method based on a deep transfer convolutional neural network (TCNN) framework. The TCNN framework is made up of an online CNN based on LeNet-5 and several offline CNNs with a shallow structure. First, time-domain signal data are converted into images that contain abundant fault information and are suitable as the input of CNN. Then, the online CNN is constructed to automatically extract representative features from the converted images and classify faults. Finally, in order to improve the real-time performance of the online CNN, several offline CNNs are also constructed and pretrained on related data sets. By directly transferring the shallow layers of the trained offline CNNs to the online CNN, the online CNN can significantly improve the real-time performance and successfully address the issue of achieving the desired diagnostic accuracy within limited training time. The proposed method is validated on two bearing data sets and one pump data set, respectively. The prediction accuracy of the proposed method using three data sets are 99.88%, 99.13%, and 99.98%, respectively. The experimental results also indicate that the improvement of accuracy is 19.21% for the motor bearing case, 29.82% for the rolling mill bearing case, and 33.26% for the pump case during the early stage of learning.

Subfields: Control and Systems Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/192506a7d94eae4419039877308a485c49102e85

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. Chuang Sun
2. Xuefeng Chen
3. Ruqiang Yan
4. Yihao Xue
5. Mengjie Huang
6. Shibin Wang
7. Rui Yang
8. Zidong Wang

In Mechanical Engineering

1. Shibin Wang
2. Chuang Sun
3. Ruqiang Yan
4. Xuefeng Chen
5. Zhibin Zhao

Citations in the corpus

• Deep Learning Algorithms for Rotating Machinery Intelligent Diagnosis: An Open Source Benchmark Study (2020, methodology): “For transfer learning, the pretrained and fine-tuned approach was used to leverage the prior knowledge from the source task in [62–64].”
• Applications of Unsupervised Deep Transfer Learning to Intelligent Fault Diagnosis: A Survey and Comparative Study (2019, methodology): “[51] pre-trained the offline CNN on the source domain and directly transferred them to the shallow layers of the online CNN via fine-tuning the online CNN on the target domain for online IFD.”
• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “[81], [82], [83] or RGB images [79], [84], researchers applied the pretrained CNN structure using large-scale image datasets,”