Open AccessApproach and Application of Semi-Blind Source Separation for Aero-Engine Vibration Signals Using ICA-R
Author(s) -
Liang Shi,
Yankai Wang,
Mingfu Liao,
Yunfan Jiang
Publication year - 2019
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 - 10.1088/1742-6596/1215/1/012030
Subject(s) - blind signal separation , vibration , independent component analysis , computer science , signal (programming language) , source separation , signal processing , fastica , algorithm , control theory (sociology) , artificial intelligence , acoustics , telecommunications , physics , channel (broadcasting) , radar , control (management) , programming language
In this paper, the approach and application of semi-blind source separation (SBSS) in aero-engine vibration signal is studied. Firstly, the features of aero-engine vibration signal and difficulties for blind source separation (BSS) are summarized, and the SBSS incorporated the available prior knowledge is match to the goal of signal processing. Then, the ICA with reference (ICA-R) algorithm based on classical FastICA is introduced, with Newton iteration and gradient descent iteration approach to obtain optimal solution. The unique parameters in ICA-R for aero-engine vibration signal are also provided. Finally, the efficacy and the accuracy of the ICA-R algorithm are verified by numerical simulations and real engine vibration signals. The approach of SBSS in this paper perfectly suited to handle aero-engine vibration source separation and it lead to efficient implementation in fault diagnosis.