Wavelet Packet-based Kernel Extreme Learning Machine for Sensor Faults Diagnosis of Hypersonic Vehicle

Sensor faults diagnosis is an important research field of the Hypersonic Vehicle (HV). A fast and effective fault diagnosis method can reduce the probability of flight accidents and improve the reliability of vehicle. Aiming at the uncertainty of the HV model and the rapidity of fault diagnosis, wavelet packet decomposition integrated with Biogeography Based Optimization Kernel Extreme Learning Machine (BBO-KELM) is proposed for the first time to identify fault. And the Biogeography Based Optimization (BBO) is used to improve the performance of the KELM firstly. The time-frequency analysis capability of wavelet packet decomposition and the high learning speed of KELM enable fast and accurate sensor faults diagnosis for the HV.