多自由度模型NOFRFs估计及损伤检测的数值模拟

基于Volterra级数理论定义的NOFRFs相当于非线性系统中的"传递函数",可很好的表征非线性系统的本质特性,是非线性检测的重要工具。结合NOFRFs的一般估算理论,考察了脉冲锤击信号的特点,进而推导了脉冲锤击激励下NOFRFs的估算方法。对一维八自由度模型的数值模拟分析,验证了该估算方法的有效性,此外,还利用前人构建的NOFRFs指标实现了对该模型系统非线性特性的检测。     

多脉冲激励法在轴承滚珠磨损中的状态研究及应用

机械故障中的轴承故障具有强的非线性特征,其故障特征提取具有一定的困难,针对此问题提出了一种基于多脉冲激励法下的Volterra级数核的故障特征提取方法。通过系统的输入输出信号并建立非线性系统的Volterra级数模型,利用时域和频域的低阶核进行轴承所处状态的对比,进而分析是否处于故障状态。以美国西储大学滚动轴承故障为例来验证该算法,应用小波算法和多脉冲激励法进行对比,得出多脉冲激励法能够较为方便准确地提取轴承的故障特征。     

非线性齿轮系统单齿故障动力学特性

针对齿轮系统运行过程中具有非线性动力学特性,借鉴混沌振子检测理论中根据系统相轨变化检测信号的原理,分析了齿轮单齿故障冲击信号出现的成因及其出现故障后非线性动力学特性的变化,建立了基于冲击分析的非线性齿轮系统单齿故障动力学模型。通过分析发现,齿轮系统模型在一定的参数条件下,其动力学特性会进入混沌状态。而在相同参数条件下,出现单齿冲击故障并达到一定程度后,齿轮系统会在故障冲击的激励下进入大周期运动,从而表现出明显异于无故障条件下齿轮系统的动力学特性。仿真结果表明,该方法能有效区别齿轮系统单齿故障状态。     

基于双谱熵和聚类分析的转子系统故障诊断

转子系统在故障状态下的振动信号往往呈现很强的非线性,其在频域上主要表现为不同频率之间相互耦合,产生合频、差频等组合频率。为了解决传统频谱分析只关注信号中的频率成分及其幅值大小,而忽略信号相位信息的问题,采用双谱方法对振动信号进行分析。双谱包含信号相位信息并且对非线性敏感,可以将早期故障的微弱非线性放大,检测出频谱中不同频率之间的非线性相位耦合关系。通过对ZT-3转子实验台植入不同类型的故障,采集系统在不同状态下的加速度信号,从振动信号的双谱中提取各频段的信息熵,采用模糊聚类方法进行故障识别。结果表明,双谱熵作为特征参量可以准确识别转子系统的故障类型,验证了方法的可行性。     

航空相机扫描镜系统的线性近似模型辨识

为深入认识摩擦对航空相机扫描镜系统频率响应特性的影响,获得更准确描述该系统真实动态的模型,使用随机相位多正弦信号测量了扫描镜系统的频率响应特性并进行了线性近似参数模型辨识.首先,介绍扫描镜系统辨识的实验平台与激励信号选择.然后,使用奇-奇频率随机相位多正弦信号分别测量扫描镜系统在非激励频率处和激励频率处的输出对输入信号幅值的依赖,从而定量评估摩擦非线性的影响.最后,基于信号采样均值及噪声采样方差、协方差估计辨识了扫描镜系统线性近似参数模型.实验结果表明,扫描镜系统的摩擦非线性主要出现在奇频率处,高于噪声10dB;系统的频率响应特性依输入信号幅值不同而各异,在低于20 rad/s频率区该差别尤为显著.由于摩擦非线性影响,扫描镜系统需要使用3阶模型描述;与正弦扫描方法相比,基于多正弦信号激励获得的参数模型可更好地描述扫描镜系统真实动态特性.得到的结果为控制器的设计奠定了基础.     

非线性转子系统碰摩故障的突变性能分析

采用突变理论对非线性转子系统碰摩故障的突变性能进行了定量研究,用平均法推导了非线性转子系统碰摩故障的频率响应方程,建立了碰摩故障的尖点突变流形和分叉集,确定了激励频率、偏心距、轴刚度非线性系数等导致系统突变的重要影响因子,分析了影响因子变化与突变发展的规律,提出了避免突变发生的预防措施,并通过数值计算方法验证了以上分析结论.     

某重型卡车驾驶室振动测试与诊断

针对某重型卡车驾驶室在各常用车速下振动过大的问题,搭建了相应的振动测试平台,对其振动特性进行了研究分析.通过对该车驾驶室、各悬置处常用车速下振动进行试验测试,分析了驾驶室振动信号,得到了振动响应的主要频率成分;分析了各信号之间相关性和振动子系统的传递特性,得到了板簧、悬置、驾驶室和车体的振动传递效果;分析了影响整车振动的主要激励源特性,结合信号响应的频域组成和系统传递特性,综合得到造成驾驶室振动过大的主要激励源为车轮,同时板箦和车体的传递特性有待改善.     

齿轮系统谐波共振的多尺度分析方法

考虑齿轮啮合动态刚度、传递误差、齿侧间隙等非线性因素,将时变刚度按5次谐波展开,齿侧间隙按3次多项式拟合,运用多尺度方法分析了单对直齿轮传动系统的谐波共振响应特性,讨论了系统在非共振硬激励下消去长期项的条件,给出了系统中存在的多种频率因子,发现了系统中存在2阶、3阶超谐波共振和1／2阶、1／3阶次谐波共振,推导了稳态振动下的频率响应方程,并绘制了频率响应曲线,分析了静态激励、动态激励、参数激励以及系统中阻尼对稳态响应的不同影响作用。     

基于相干检测解轨迹跟踪的非线性电路故障诊断

通过调幅激励下非线性电路响应随凋幅信号变化火系(解轨迹)的跟踪,提出一种基于相干检测解轨迹跟踪的非线性电路故障诊断方法.利用解轨迹与Volterra响应间的故障信息等价性,将非线性故障诊断问题变换到线性子电路上分析;南解轨迹与调幅信号间的相干性.通过相位驻留法提取动态电路的静态特征,并建立统一的故障诊断方程.最后通过实例仿真说明该方法的有效性.     

基于傅立叶变换的频域调节器在往复运动中的运用

基于系统频率响应特性,在分析往复运动具有周期性特点的基础上,设计了一种新型的基于傅立叶变换的频域调节器,将此作为系统调节器的前馈环节.频域调节器通过调节选定的频域信号的幅值和相角,来调整系统调节器的输出,从而减少系统误差,改善系统控制性能.并通过实验验证了所提出的基于傅立叶变换的频域调节器的有效性.     

Decoupling analysis on nonlinear system based on the modified generalized frequency response functions

This paper presents a nonlinear decoupling approach based on the Modified Generalized Frequency Response Functions (MGFRFs) and the nonlinear feature of phase invariance, for the pure nonlinearity-input nonlinear system. The MGFRFs are defined by combining the ‘homotopy’ GFRFs and phase information of the system input. The nonlinear feature of phase invariance is extracted based on MGFRFs. The decoupling approach is proposed based on MGFRFs and extended from the pure tone excitation to the multi-tone excitations by considering phase invariance. Numerical simulation and experimental investigation were carried out, whose results have shown that nonlinear feature of phase invariance is correct and reasonable and the proposed decoupling approach is valid and feasible. The proposed decoupling approach can be employed to identify the excitation sources and to estimate nonlinear system parameters for the pure nonlinearity-input nonlinear vibration system.     

基于最大故障信息量二元树的模拟电路诊断法

本文提出一种模拟电路故障诊断法。利用二元树的信息传递性实现模拟电路的故障定位，寻找系统Y在N个故障状态X下的最大故障信息量J0，采用序贯法一直寻找不同故障条件下子系统特征xj的最大信息量Ji，最终找到一个故障特征群R；构造系统最大故障信息二元树，从故障特征群中快速定位故障点，实现模拟电路故障的有效诊断。最后给出一个诊断实例验证了该方法。     

相关小波法在结构系统识别中的应用

对于一个线性结构系统，从输入输出数据中提取精确的脉冲响应函数和频响函数是对系统进行动力学建模和参数识别的关键步骤。在循环小波变换的基础上，研究了提取脉冲响应函数的相关小波法及其相应的平均算法。数值仿真结果表明，对于不同的激励信号，相关小波法都能够获得比FFT方法精度更高的脉冲响应函数和频响函数，具有更大的优越性。利用悬臂梁实验验证了相关小波法在实际结构中的正确性和有效性。     

Evaluation of transmissibility for a class of nonlinear passive vibration isolators

In this study, the concept of Output Frequency Response Functions (OFRFs) is applied to represent the transmissibility of nonlinear isolators in frequency domain. With the OFRFs estimated from numerical simulation responses, an explicit analytical relationship between the transmissibility and the nonlinear characteristic parameters is derived for a wide class of nonlinear isolators that have nonlinear anti-symmetric damping characteristics and a comprehensive pattern about how the nonlinear damping characteristic parameters might affect the force and displacement transmissibility is built for the vibration isolators. The results reveal that it is reasonable to analyze the force and displacement transmissibility of the nonlinear isolators by simply investigating the fundamental harmonic components of the force and displacement outputs of the nonlinear isolators, and the introduction of a nonlinear anti-symmetric damping into vibration isolators can significantly suppress both the force and displacement transmissibility over the resonant frequency region, but has almost no effect on the transmissibility at non-resonant regions. These conclusions are of significant importance in the analysis and design of the nonlinear vibration isolators with nonlinear anti-symmetric damping.     

基于BP神经网络与瞬态响应分析的电路故障诊断技术

论文研究了基于模式识别原理的人工神经网络电路故障诊断方法,结合仿真技术,应用瞬态响应分析把数模混合信号电路中数字电路部分与模拟电路部分统一进行诊断,探讨了模拟电路和数字电路特性相差太大难于诊断的问题。针对具体电路,利用MATLAB神经网络工具箱对电路进行仿真,结果表明,利用所提出的实现方法对模拟及数模混合电路进行了较准确的诊断,达到了预期的目的。     

Feasibility study of structural damage detection using NARMAX modelling and Nonlinear Output Frequency Response Function based analysis

Nonlinear Output Frequency Response Functions (NOFRFs) are a series of one-dimensional functions of frequency recently proposed by the authors to facilitate the analysis of nonlinear systems in the frequency domain. The present study is concerned with a feasibility study of the application of the well-known Nonlinear Auto-Regressive Moving Average with eXogenous Inputs (NARMAX) modelling method and the NOFRFs-based analyses to the detection of damage in engineering structures. The new technique includes three steps. First, a NARX model is established by applying the NARMAX modelling method to input and output data collected from a test on an inspected structure. Then, the NOFRFs and an associated index for the inspected structure are determined from the established NARX model. Finally, structural damage detection is conducted by comparing the values of the NOFRF index of the inspected structure with the values of the index for a damage-free structure. An experimental application to the detection of damage in aluminium plates demonstrates the potential and effectiveness of the new damage detection technique.     

Fault diagnosis for a class of nonlinear systems via ESO

In this paper, a novel fault detection and identification (FDI) scheme for a class of nonlinear systems is presented. First of all, an augment system is constructed by making the unknown system faults as an extended system state. Then based on the ESO theory, a novel fault diagnosis filter is constructed to diagnose the nonlinear system faults. An extension to a class of nonlinear uncertain systems is then made. An outstanding feature of this scheme is that it can simultaneously detect and identify the shape and magnitude of the system faults in real time without training the network compared with the neural network-based FDI schemes. Finally, simulation examples are given to illustrate the feasibility and effectiveness of the proposed approach.     

基于SVR的非线性系统故障诊断研究

针对非线性系统辨识建模和故障诊断难的问题,利用回归型支持向量机(support vector regression,SVR)分别设计了非线性系统的辨识建模系统和故障诊断系统,最后以某一非线性系统为例进行了仿真试验研究,建立了该非线性系统的SVR辨识模型,在此基础上进行了三种典型故障的诊断试验,仿真试验结果验证了该方法的有效性和先进性。     

Signal-Based Intelligent Hydraulic Fault Diagnosis Methods: Review and Prospects

Hydraulic systems have the characteristics of strong fault concealment,powerful nonlinear time-varying signals,and a complex vibration transmission mechanism;hence,diagnosis of these systems is a challenge.To provide accurate diagnosis results automatically,numerous studies have been carried out.Among them,signal-based methods are commonly used,which employ signal processing techniques based on the state signal used for extracting features,and further input the features into the classifier for fault recognition.However,their main deficiencies include the following:(1)The features are manually designed and thus may have a lack of objectivity.(2) For signal processing,feature extraction and pattern recognition are conducted using independent models,which cannot be jointly optimized globally.(3) The machine learning algorithms adopted by these methods have a shallow architecture,which limits their capacity to deeply mine the essential features of a fault.As a breakthrough in artificial intelligence,deep learning holds the potential to overcome such deficiencies.Based on deep learning,deep neural networks(DNNs) can automatically learn the complex nonlinear relations implied in a signal,can be globally optimized,and can obtain the high-level features of multi-dimensional data.In this paper,the main technology used in an intelligent fault diagnosis and the current research status of hydraulic system fault diagnosis are summarized and analyzed.The significant prospect of applying deep learning in the field of intelligent fault diagnosis is presented,and the main ideas,methods,and principles of several typical DNNs are described and summarized.The commonality between a fault diagnosis and other issues regarding typical pattern recognition are analyzed,and research ideas for applying DNNs for hydraulic fault diagnosis are proposed.Meanwhile,the research advantages and development trend of DNNs(both domestically and overseas) as applied to an intelligent fault diagnosis are reviewed.Furthermore,the fault characteristics of a complex hydraulic system are summarized and discussed,and the key problems and possible research ideas of applying DNNs to an intelligent hydraulic fault diagnosis are presented and comprehensively analyzed.     

基于电机电流分析的万能式断路器机械故障诊断

电机电流信号常用于分析电动机本身的故障问题,但对其应用于与电机相连机构的故障分析的研究较少。提出一种基于储能电机电流分析的万能式断路器操作机构故障诊断方法。首先采用Hilbert幅值解调法和改进的小波包阈值法相结合获取交流电流信号的包络线,以解决随机噪声干扰造成的所提取包络线粗糙的问题;然后通过包络线提取电流信号的时间量、电流量以及峭度作为不同故障状态电流波形的特征参数;最后融合模糊聚类和量子粒子群优化的相关向量机实现对断路器正常状态、传动齿轮卡涩、储能弹簧卡涩以及脱落的4种状态的辨识。构建了基于电流分析的万能式断路器故障诊断系统,在不同工况下进行了验证,结果表明该方法能有效提取操作机构储能相关部件的故障特征,实现了对操作机构储能相关部件的故障诊断。