Fault Diagnosis Method Based on Gap Metric Data Preprocessing and Principal Component Analysis

Abstract EN

Principal component analysis (PCA) is widely used in fault diagnosis.

Because the traditional data preprocessing method ignores the correlation between different variables in the system, the feature extraction is not accurate.

In order to solve it, this paper proposes a kind of data preprocessing method based on the Gap metric to improve the performance of PCA in fault diagnosis.

For different types of faults, the original dataset transformation through Gap metric can reflect the correlation of different variables of the system in high-dimensional space, so as to model more accurately.

Finally, the feasibility and effectiveness of the proposed method are verified through simulation.