What question did this study set out to answer?

The aim is to identify and locate rub-impact faults using a novel method combining SVMD and ITD techniques.

February 2, 2026Noise & Vibration Worldwide0 citations

Identification and location of rub-impact faults based on SVMD-ITD

YGYingdong Gao XGXiangdong Ge MYMingyue Yu

Key Points

The aim is to identify and locate rub-impact faults using a novel method combining SVMD and ITD techniques.
Utilized SVMD for signal decomposition to obtain mode component signals.
Removed high-frequency noise components from the signals for reconstruction.
Applied ITD for further decomposition to isolate rotation components.
Calculated Gini coefficient of residual signals to represent equipment state and fault positions.
Performed clustering analysis with t-SNE and KNN for fault identification.
Achieved over 98% mean identification accuracy in independent experiments.
Demonstrated better fault identification performance compared to classical methods using the same dataset.

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%.

Ask AI

Helpful

Bookmark

View Full Paper