【作者】 陈仲生；

【导师】 温熙森；

【作者基本信息】 国防科学技术大学 ， 机械工程， 2004， 博士

【摘要】 随着直升机在现代战争中作用和地位的日益提高,它的可靠性和安全性问题越来越引起军方的高度重视。旋转部件是直升机最容易发生故障且无冗余备份的部分,一旦发生故障常常会导致灾难性的后果,因此设法尽早地对这些可能引发灾难性事故的故障进行有效检测对提高直升机的可靠性和安全性具有重要意义,而这类故障早期检测的关键技术之一就是故障信号的特征提取。 直升机旋转部件的结构复杂、工作环境恶劣,使得表征故障的特征信号较复杂,因冲击和调制所引发的动态信号往往具有非高斯和(或)非平稳性,突出的故障特征信号表现出相位耦合、冲击和循环平稳等特点。建立在高斯性和平稳性假设基础上的传统的分析方法不太适合于有效提取这些故障的特征信息。为此,发展能有效处理非高斯、非平稳特征信号的高阶统计量分析方法,并将其用于直升机旋转部件故障特征提取具有重要的实际应用价值。 本文正是在“十五”武器装备预先研究项目的资助下,以某型直升机中减速器为研究对象,利用高阶统计量理论中的高阶谱、高阶时频分布和循环统计量方法,对能突出表征直升机旋转部件故障的相位耦合、冲击和循环平稳信号的特征提取方法进行了深入的研究。主要内容包括: 1.在分析直升机旋转部件故障特征提取面临的主要问题、总结常规特征提取方法存在的主要局限的基础上,深入探讨了高阶统计量方法在直升机旋转部件故障信号特征提取中的优势及需要解决的问题。 2.针对表征直升机旋转部件故障的相位耦合信号的特征提取,研究并改进了基于高阶谱的故障特征提取方法。首先构造了一种二维Hanning-Poisson组合滞后窗,提高了双谱估计的方差性能,然后分析了基于三谱的故障相位耦合信号的特征提取方法,并利用2 1/2维谱实现了三谱的在线应用。数值分析和实验表明,双谱和三谱用于提取背景噪声下故障相位耦合信号特征是行之有效的,且三谱能弥补双谱不能处理对称分布非高斯信号的不足。 3.研究了基于高阶时频分布的故障特征提取方法,以有效提取表征直升机旋转部件故障的冲击信号的特征。首先构造了一种冲击信号增强的两级滤波预处理结构,然后利用图像处理技术定义了冲击信号的两种图像特征,能够实现冲击信号特征的自动更多还原

【Abstract】 Helicopters are becoming more and more important in modern wars, so their reliability and flying safety have attracted more attention in the military domain. Rotating components are very easy to get abnormal and often not redundant, so in-flight malfunctions can lead to catastrophic results. Thus it is much significant to try to detect such faults early to improve reliability and safety of helicopters. One of its key techniques is feature extraction of fault signals.Fault signals of rotating components in helicopters are often complex due to their complex structures and serious operating circumstances. Condition signals caused by impulses and modulations are often non-Gaussian and (or) non-stationary. Among these condition signals, the prominent fault signals show phase coupling, impulsive, cyclostationary and so on. Traditional methods are often based on Gaussian & stationary assumptions and less suitable to extract the effective features readily. So it is valuable to study how to utilize higher-order statistics to extract fault features of helicopter rotating components.Supported by the National Defense Advanced Research Projects, this dissertation takes the intermediate gearbox of one helicopter as a diagnostic object and addresses the problem of feature extraction of phase coupling, impulsive and cyclostationary signals characterizing faults of helicopter rotating components using higher-order spectra, higher-order time-frequency distribution and cyclic-statistics included in higher-order statistics theory. The detailed contents and innovative work can be summarized as follows.1. The main difficulty faced by feature extraction of helicopter rotating components is analyzed and the traditional methods are summarized. Then the advantages of methods based on higher-order statistics are given and several problems are pointed out.2. As to the problem of feature extraction of phase coupling signals characterizing faults of helicopter rotating components, the method based on higher-order spectrum (HOS) is deeply studied and improved. At first, one 2-D Hanning-Poisson combined lag window is presented, which can improve the performance of bispectrum estimation. Secondly, a novel method of feature extraction using trispectrum is discussed. Finally, 21/2-D spectrum is used for on-line application of trispectrum.Numerical and experiment analysis demonstrate that bispectrum and trispectrum candetect phase coupling features of fault signals under background noises. Trispectrum can also be used to analyze signals with symmetrical probability density compared with bispectrum.3. The method based on higher-order time-frequency distribution is deeply studied to extract features of impulsive signals characterizing faults of helicopter rotating components. One two-step filtering preprocessing method is proposed to enhance weak impulsive signals, and two kinds of image features of impulsive signals are defined using image-processing techniques, which can be used to extract and quantify impulsive features automatically.The above research shows that two-step filtering preprocessing can suppress both narrow-band interfere signals and broadband stochastic noises, so it will increase the SNR of impulsive signals. Based on it the sliced Wigner trispectrum is feasible to extract impulsive features of faults automatically and its effectiveness is validated by early detection of one gear-pitting fault.4. In order to extract features of cyclostationary signals characterizing faults of helicopter rotating components, the methods of extracting fault features using cyclic statistics are deeply studied.(1) One novel method of extracting 1-D cyclostationary features of faults is presented based on spectrum line regeneration (SLR).(2) The way of extracting 2-D cyclostationary features of faults based on spectrum correlation density (SCD) function is discussed.(3) The idea of extracting 3-D cyclostationary features of faults using cyclic bispectrum is studied. Also its estimation algorithm based on 2-D Chirp-Z transformation is presented.Numerical and experiment analysis demonstrate that cyclic statistics can extract cyclostationary features close related to faults under background noises.5. Combined with the cyclostationarity of signals of helicopter rotating components, one new method of feature extraction based on cyclostationary time series model is proposed. One linear almost periodically time-varying AR (LPTV-AR) model is presented and the criterion of ordering the model is defined. Then the algorithms to identify model parameters are put forward in both time domain and frequency domain.Numerical and experiment analysis demonstrate that the method is insensitive to additive stationary noise and can be used to detect and predict "novel" abnormal conditions of helicopter rotating components.更多还原