Open AccessBlind source separation of rotor vibration signals in high-noise environments
Author(s) -
Yinjie Jia,
Pengfei Xu,
Zhijian Wang,
Ping Zong
Publication year - 2021
Publication title -
revista internacional de métodos numéricos para cálculo y diseño en ingeniería
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 9
eISSN - 1886-158X
pISSN - 0213-1315
DOI - 10.23967/j.rimni.2020.10.008
Subject(s) - vibration , rotor (electric) , noise (video) , fault (geology) , computer science , signal (programming language) , blind signal separation , noise, vibration, and harshness , autocorrelation , acoustics , source separation , control theory (sociology) , engineering , speech recognition , physics , mathematics , artificial intelligence , telecommunications , electrical engineering , statistics , seismology , geology , image (mathematics) , channel (broadcasting) , control (management) , programming language
During the operation of the engine rotor, the vibration signal measured by the sensor is the mixed signal of each vibration source, and contains strong noise at the same time. In this paper, a new separation method for mixed vibration signals in strong noise environment (such as SNR=-5dB) is proposed. Firstly, the time-delay autocorrelation de-noising method is used to de-noise the mixed signals. Secondly, one common algorithm (the MSNR algorithm is used here) is adopted to separate the mixed vibration signals, which can improve the separation performance. The simulation results verify the validity of the method. The proposed method provides a new idea for health monitoring and fault diagnosis of engine rotor vibration signals.