Identification and location of rub-impact faults based on SVMD-ITD.
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| Title: | Identification and location of rub-impact faults based on SVMD-ITD. |
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| Authors: | Gao, Yingdong1 (AUTHOR), Ge, Xiangdong2 (AUTHOR), Yu, Mingyue1 (AUTHOR) 20140023@sau.edu.cn, Li, Zhaohua1 (AUTHOR) |
| Source: | Noise & Vibration Worldwide. Jun/Jul2026, Vol. 57 Issue 6/7, p496-506. 11p. |
| Subjects: | Fault diagnosis, Signal separation, Gini coefficient, Mechanical failures, Cluster analysis (Statistics) |
| Abstract: | To exactly identify and locate a rub-impact fault, the paper starts with the perspective of dual composition and proposes a method based on the combination of successive variational mode decomposition (SVMD) and intrinsic time scale decomposition (ITD). Firstly, signals are decomposed through SVMD and corresponding mode component signals can be obtained; concerning that most noises are distributed in high frequency, a component signal of highest frequency is removed and signals are reconstructed based on residual components. Secondly, the reconstructed signal is decomposed again through ITD and proper rotation components can be obtained, and a component signal of highest frequency is removed again. Thirdly, the running state of equipment and rub-impact positions are represented by Gini coefficient of residual component signals and feature vectors are constructed. Finally, clustering analysis is given to the feature vectors by t-SNE and k-nearest neighbor (KNN) is applied for identifying a rotor-stator rubbing failure and affected positions. The proposed method is compared with classical schemes based on the same data to verify its effectiveness. The result indicates that the proposed SVMD-ITD method has better fault identification performance. In two independent experiments, the mean identification accuracy of 10 successive random tests is above 98%. [ABSTRACT FROM AUTHOR] |
| Copyright of Noise & Vibration Worldwide is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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