KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery
| dc.contributor.author | Hu, Xiao | |
| dc.contributor.author | Xiao, Zhihuai | |
| dc.contributor.author | Liu, Dong | |
| dc.contributor.author | Tang, Yongjun | |
| dc.contributor.author | Malik, O. P. | |
| dc.contributor.author | Xia, Xiangchen | |
| dc.date.accessioned | 2020-07-05T08:00:09Z | |
| dc.date.available | 2020-07-05T08:00:09Z | |
| dc.date.issued | 2020-06-29 | |
| dc.date.updated | 2020-07-05T08:00:09Z | |
| dc.description.abstract | Feature extraction plays a key role in fault diagnosis of rotating machinery. Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model. To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals. First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum. Next, the spectrum is divided into several segments. Then, local-global feature extraction is performed by applying KPCA to these segments. Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature. The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery. A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method. Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed. By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified. | |
| dc.description.version | Peer Reviewed | |
| dc.identifier.citation | Xiao Hu, Zhihuai Xiao, Dong Liu, Yongjun Tang, O. P. Malik, and Xiangchen Xia, “KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery,” Mathematical Problems in Engineering, vol. 2020, Article ID 5804509, 17 pages, 2020. doi:10.1155/2020/5804509 | |
| dc.identifier.uri | http://dx.doi.org/10.1155/2020/5804509 | |
| dc.identifier.uri | http://hdl.handle.net/1880/112246 | |
| dc.identifier.uri | https://dx.doi.org/10.11575/PRISM/37968 | |
| dc.language.rfc3066 | en | |
| dc.rights.holder | Copyright © 2020 Xiao Hu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. | |
| dc.title | KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery | |
| dc.type | Journal Article |