Open AccessThe Characteristic Frequency Extraction of Helicopter Fault Signal Based on AR Model Estimation
Author(s) -
Penghui Niu,
Zhen Wang,
Ke Jia,
Suyong Liu
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1676/1/012183
Subject(s) - fault (geology) , signal (programming language) , spectral density estimation , reliability (semiconductor) , extraction (chemistry) , computer science , noise (video) , time–frequency analysis , sampling (signal processing) , fourier transform , algorithm , artificial intelligence , mathematics , power (physics) , telecommunications , radar , physics , mathematical analysis , chemistry , chromatography , quantum mechanics , seismology , image (mathematics) , programming language , geology , detector
It is very important to extract the characteristic frequency of helicopter mechanical fault signal, which is directly related to the accuracy of fault diagnosis and the reliability of early fault prediction. The traditional characteristic frequency extraction of fault signal mostly uses the spectrum analysis method based on Fourier analysis. The algorithm of this method is simple, but has high requirements on SNR (signal-to-noise ratio) and sampling time. In this paper, researchers use the AR model spectral estimation method based on the modern spectral estimation theory to study the characteristic frequency extraction of helicopter mechanical fault signal. The simulation results show that the effect is better.