Fault Diagnosis of Tiltrotor Aircraft via Improved Discrete Wavelet-OMELM

doi:10.3969/j.issn.1000-1093.2014.11.025

Abstract

Abstract: An improved discrete wavelet-optimization method-based extreme learning machine (OMELM) algorithm is presented for the fault diagnosis of flight control system in tiltrotor aircraft. An adaptive heuristic wavelet denoising method is used to denoise the sampled signal. Feature vector of each layer is extracted using Parseval energy after the discrete wavelet decomposition of fault signal. The energy feature is normalized as the improved OMELM network input, and then the actuator fault models is classified using the improved OMELM network. Finally, an XV-15 tiltrotor aircraft mode is validated by simulation. The results show that the method has a higher average recognition rate, and needs a short diagnosis time.

Key words: aerospace system engineering, tiltrotor aircraft, fault diagnosis, discrete wavelet transform, optimization method-based extreme learning machine, adaptive heuristic wavelet denoising

CLC Number:

V275⁺.1

YAN Feng, CHEN Xiao, WANG Xin-min, PENG Cheng, HU Ya-zhou. Fault Diagnosis of Tiltrotor Aircraft via Improved Discrete Wavelet-OMELM[J]. Acta Armamentarii, 2014, 35(11): 1914-1921.

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