汽车发动机非稳态振动信号分析方法比较研究

采用短时Fourier变换、小波包AR谱和二阶循环谱方法分别对发动机加速信号进行了时频分析,提取了分析对象的故障特征,并对3种方法进行了比较研究。研究表明:短时Fourier变换的频率分辨特性相对较差;小波包对单个故障有良好的分辨特性,但对多个故障的分辨能力较差;而循环谱方法有更好的频率分辨特性,且在特征频率处有更显著的谱峰。

船用柴油机非稳态振动信号分析系统

船舶柴油机作为一个多变复杂、多层次的动力系统,其表面振动信号直接受内部零部件工作状态的影响。非稳态振动信号可以用来反映系统内部的运行状况,从而可实时监测柴油机运行状况,因而,准确、多元的提取出振动信号特征是关键步骤。本文主要使用数据分析软件MATLAB对非稳态振动信号进行时域、频域特征参数提取,分析气阀磨损对振动信号的影响规律,并基于此构建船舶柴油机非稳态振动信号分析系统。

船用柴油机振动信号气阀磨损预测系统

船舶柴油机在运行过程中变化复杂,受到内部各部件的振动、冲击、摩擦及燃烧等激励作用,缸盖会产生非稳态振动信号。非稳态振动信号可以实现相应零部件及燃烧状态的故障预测,气阀是柴油机的重要部件,通过振动信号来分析气阀磨损情况具有重大意义。本文基于实测以及有限元模拟得到不同磨损状况下的缸盖振动信号,通过对其进行分析,得到对应磨损程度下的特征参数,使用数据分析软件MATLAB构建特征参数与气阀磨损量之间的BP神经网络模型,并基于此模型构建船用柴油机振动信号气阀磨损预测系统。

Hilbert-Huang transform based noise filtering for the identification of structural systems

This paper proposes an enhanced noise filtering technique to the Hilbert-Huang transformation technology for the identification of structural systems using vibration measurements. The Hilbert-Huang transformation technology is a set of superior algorithms non-stationary signals and also for analyzing non-linear and offers increased accuracy for linear and stationary signals. However, the signals are filtered by reconstruction from ＂＇selected＂ intrinsic mode functions （IMFs）, derived from the original signal through the empirical mode decomposition method. The proposed filtering technique offers the criterion for selecting the IMFs using the orthogonalization coefficient. In addition, a simple free vibration modal analysis has been resolved for the evaluation of the modal damping ratio. Through the enhanced filtering, it is possible to increase the accuracy for the estimation of the time-varying system＇s natural frequency and the damping ratio, indicative of the degree of damage, which helps an effective design of a control system.