EEMD和分形组合技术对ECS涡轮轴承故障特征提取的研究
Study on Fault Feature Extraction of ECS Turbine Bearing by Combination of EEMD and Correlation Dimension

针对飞机环控涡轮轴承运行时的非线性动力学特性，为了更加准确地分析轴承的故障，从振动信号分析的角度，提出基于EEMD和分形维数相结合的轴承状态特征量提取方法。先对轴承正常、内圈故障、外圈故障和保持架故障等不同运行状态下的振动信号进行EEMD分解，滤除噪声信号，提高信噪比，以减小背景噪声对分形的不利影响。然后对去噪信号再进行相空间重构，计算其关联维数并进行对比分析。实验结果表明：关联维数作为非线性几何不变量可以作为环控涡轮轴承运行状态的特征量；该方法能够准确有效地识别轴承的运行状态。
Aiming at the nonlinear dynamic characteristics of turbine bearings in aircraft environment control system, a state feature extracting method for bearings based on EEMD (Ensemble empirical mode decomposition) and fractal dimension from the angle of vibration signal processing is proposed to analyze the bearing fault more accurately. Firstly, vibration signals under different operating conditions, such as normal bearing, inner ring fault, outer ring fault and cage fault, are decomposed by EEMD to filter noise signal and advance signal-to-noise so as to reduce the adverse effect of background noise on fractal. Then correlation dimension of those signals phase is calculated, contrasted and analyzed after space reconstruction. The experimental results show that, the correlation dimension, as nonlinear geometric invariants, can be used as the characteristic quantity of ECS (Environment control system) turbine bearing on running state. Moreover, this method can accurately and effectively identify the running state of the bearing