Diagnosis of Sensor Faults in Hypersonic Vehicles Using Wavelet Packet Translation Based Support Vector Regressive Classifier

In this article, a fault diagnosis scheme based on the data-driven approach of hypersonic vehicles (HVs) is studied. First, the fault features are obtained by wavelet packet translation (WPT) processing. Second, an improved distance evaluation technique (DET) based on Spearman correlation analysis is used to select features and reduce dimensions. The designed enhanced WPT-DET (EWPT-DET) method can extract sensitive features based on self-defined attention coefficients. Third, the fault pattern recognition process is achieved by support vector regression (SVR) with genetic algorithm optimization. The SVR classifier with high dimensional linear fitting ability is very suitable for HVs' reaction control system with sensor faults. The method is further used in locating and diagnosing multisensor fusion faults. Moreover, the fault occurrence time of single sensor timing faults is judged. Finally, simulation studies are provided to illustrate the enhanced performance of the proposed approach.

Automated summary

This article investigates a fault diagnosis scheme for hypersonic vehicles based on a data-driven approach. It uses wavelet packet translation and an improved distance evaluation technique to extract sensitive features, and utilizes support vector regression for fault pattern recognition and localization.