Loading...
Please wait, while we are loading the content...
Similar Documents
Unsupervised Domain Adaptive 1D-CNN for Fault Diagnosis of Bearing
| Content Provider | MDPI |
|---|---|
| Author | Shao, Xiaorui Kim, Chang-Soo |
| Copyright Year | 2022 |
| Description | Fault diagnosis (FD) plays a vital role in building a smart factory regarding system reliability improvement and cost reduction. Recent deep learning-based methods have been applied for FD and have obtained excellent performance. However, most of them require sufficient historical labeled data to train the model which is difficult and sometimes not available. Moreover, the big size model increases the difficulties for real-time FD. Therefore, this article proposed a domain adaptive and lightweight framework for FD based on a one-dimension convolutional neural network (1D-CNN). Particularly, 1D-CNN is designed with a structure of autoencoder to extract the rich, robust hidden features with less noise from source and target data. The extracted features are processed by correlation alignment (CORAL) to minimize domain shifts. Thus, the proposed method could learn robust and domain-invariance features from raw signals without any historical labeled target domain data for FD. We designed, trained, and tested the proposed method on CRWU bearing data sets. The sufficient comparative analysis confirmed its effectiveness for FD. |
| Starting Page | 4156 |
| e-ISSN | 14248220 |
| DOI | 10.3390/s22114156 |
| Journal | Sensors |
| Issue Number | 11 |
| Volume Number | 22 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2022-05-30 |
| Access Restriction | Open |
| Subject Keyword | Sensors Industrial Engineering Fault Diagnosis Vibration Signal 1d-cnn Domain Adaption Autoencoder |
| Content Type | Text |
| Resource Type | Article |
