船体振动的轴系周期性故障特征信息识取方法

针对船舶推进轴系早期碰摩故障冲击信号周期性强且易被强烈的背景噪声所淹没的问题，提出了基于船体尾部结构振动的轴系周期性故障特征信息识取方法，简称EEAF（EEMD + Autocorrelation Analysis + FFT）。首先，对采集的复杂船体尾部结构振动信号进行集合经验模态分解（Ensemble Empirical Mode Decomposition，EEMD），得到一系列固有模式分量（IMF）；再以自相关函数的性质为准则，筛选出存在周期成分的IMF分量；最后对相应分解层进行快速傅里叶变换，频谱分析识取表征轴系早期碰摩故障的特征量。通过轴系故障的仿真和实船试验研究，验证了该方法的有效性和可行性。

Recognition Method for Shaft Fault Feature Information based on Ship Hull Vibration

Aiming at the problem that the impact signal of ship propulsion shafting early rubbing fault has strong periodicity and is easily submerged by strong background noise, a method for identifying the characteristic fault information of the shafting based on the structural vibration of the hull is proposed, which is called EEAF (mean: EEMD + Autocorrelation Analysis + FFT). Firstly, Ensemble Empirical Mode Decomposition (EEMD) is performed on the collected complex hull structure vibration signal to obtain a series of inherent mode components (IMF). Then, guided by the nature of autocorrelation function, screening out the IMF component of the periodic component; Finally, the fast Fourier transform is performed on the corresponding decomposition layer, and the spectrum analysis is used to identify the feature quantity of the early rubbing fault of the shafting. The effectiveness and feasibility of the proposed method are verified by the simulation of the shafting fault and the actual ship test.