风力机叶片蒙皮初始裂纹的盲信号提取

针对风力机叶片初始裂纹特征难以提取的问题,提出了一种逐步提取并消减噪声源信号从而获得微弱裂纹故障特征的盲提取方法.首先基于卷积混合模型极小化改进代价函数推导自适应学习迭代算式,在仿真实验中确定非线性激励函数和滤波器的传输函数,根据输出信号的性能参数证明了改进算法对尖峰噪声的异常点更加敏感稳健.在风力机叶片疲劳实验台上模拟叶片蒙皮的初始横向裂纹,通过声发射信号采集系统获得观测信号,分析噪声源的特性并提取了初始裂纹的声发射信号特征,为风力机叶片状态监测和预警提供了依据.     

单通道AE信号盲分离的飞机构件监测方法研究

基于声发射(AE)技术的飞机结构件疲劳裂纹检测是飞机健康状态识别的一种有效方法。由于声发射信号的瞬态性、不确定性、微弱性和易受机电干扰性,使声发射检测技术很大程度上已演变成信号处理问题,目前多数研究报道的是单源声发射信号的降噪处理。然而,在实际应用中,不仅结构件出现疲劳裂纹时会产生AE信号,而且紧耦合的结构体之间也会因冲击载荷产生弹性波,以至观测信号一般是多源AE混合信号,波的传播时延的存在使得信号混合方式为卷积混合。针对目前测试方法不能正确识别AE信号,以致难以识别结构体是否存在疲劳裂纹的问题,提出一种具有信号源个数估计的单通道非负矩阵分解解卷积盲源分离算法。首先采用经验模态分解方法将单通道混合信号分解为多个本征模态函数;然后采用主成分分析法估计信号源个数,并重构观测信号;最后通过非负矩阵分解解卷积得到各个源信号。实验结果表明,单通道盲源分离算法能正确分离AE信号,为飞机关键结构件的疲劳裂纹监测提供了一种方法。     

基于小波能谱系数的风力机疲劳裂纹特征

提出了风力机叶片裂纹扩展声发射信号的优化小波重分配尺度谱及小波能谱系数相结合的分析法。基于Shannon熵理论计算裂纹扩展声发射信号的重分配尺度谱小波基函数带宽参数,得到最适合裂纹声发射信号的Morlet小波基函数。用优化后的小波基函数计算重分配尺度谱,获得裂纹扩展特征成分在时间尺度平面的高幅值能量分布,利用特征能谱系数表征其重分配尺度谱的特征。实验结果表明,该方法有良好的时频聚集性和抗噪能力,实现了风力机叶片裂纹扩展声发射信号的时频特征提取,得到了能谱系数作为特征向量表示信号特征。该方法可用来实现风力机叶片在复杂环境中的模式识别。     

核函数主元分析及其在齿轮故障诊断中的应用

提出了基于核函数主元分析的齿轮故障诊断方法。该方法通过计算齿轮振动信号原始特征空间的内积核函数来实现原始特征空间到高维特征空间的非线性映射。通过对高维特征数据作主元分析,得到原始特征的非线性主元,以所选的非线性主元作为特征子空间对齿轮工作状态进行分类识别。用齿轮在正常状态、裂纹状态和断齿状态下的试验数据对该方法进行了检验,比较了主元分析与核函数主元分析的分类效果。结果表明,核函数主元分析能有效的检测裂纹故障的出现,正确区分不同的故障模式,更适于提取故障信号的非线性特征。     

风力机叶片裂纹的动态扩展特性研究

首先推导了风力机叶片裂纹尖端塑性区的应力强度因子,确定了影响裂纹扩展的主要因素,然后根据裂纹扩展时的声发射信号特性,探讨了声发射信号的特征提取方法。在试验中分析了不同试件的裂纹形貌特性,并针对采集的裂纹扩展声发射信号,比较了不同小波基函数对声发射信号特征提取的影响,提取了裂纹动态扩展的特征频率,从而建立了声发射特征参数与声发射源之间的关联机制,为风力机叶片状态监测和早期预警提供理论依据。     

金属板材拉深成型裂纹识别实验

提出一种采用声发射(Acoustic Emission,AE)技术对金属板材拉深件的成型状态进行识别的方法。在此实验过程中,首先采集金属板材拉深成型时的裂纹AE信号,然后用独立分量分析法对其进行分离,进而采用时序分析法提取裂纹AE信号的特征参数-能量值,最后根据模糊综合评判法辨别制件是否有裂纹产生。通过实验结果分析可知,采用AE技术能够准确地监测产生裂纹的初始和扩展过程,也能有效识别拉深件成型状态,为拉深成型件的质量判断提供了有力依据。     

风力机叶片疲劳裂纹AE信号的小波变换优化方法

为实现风力机叶片的及时有效地监测和维护,使用声发射技术采集疲劳裂纹信号,从而提取裂纹特征。而声发射信号的突发性和冲击性需要具有时频分析能力的信号处理方式来提纯和降噪,小波变换方法作为常用的时频处理方式油漆有效性,但是现有的小波基函数不足以适应该信号的分析。提出基于Shannon熵理论计算疲劳裂纹扩展的声发射信号的小波基函数带宽参数,得到最适合此裂纹声发射信号的Morlet小波基函数,计算优化基函数的小波,获得风力机疲劳裂纹特征成分在时间尺度平面的高幅值能量分布。实验研究表明,优化小波基的方法具有很好的时频聚集性和抗噪能力,实现了风力机叶片裂纹声发射信号的时频特征清晰准确的提取。     

多振源卷积混合的时域盲源分离算法

在机械多源振动传播和卷积混合模型的基础上,提出一种基于时域的多振源卷积混合信号的盲源分离算法.该算法以独立性为评判准则,采用反向学习和合理简化滤波器系数的方式,进行滤波器系数的学习,进而实现基于时域的多振源卷积混合信号的分离.仿真试验和多机振动源试验结果表明,该算法对于多源卷积混合信号具有很好的分离效果,可应用于机械设备多激振源卷积混合情况下机械振动源信号的有效分离.     

一种利用振动响应非线性估计的叶片裂纹定位方法

对叶片裂纹进行准确定位,对于实现准确维修延长其工作寿命具有重要的意义。针对叶片裂纹定位问题,提出一种高频激励下利用多位置点振动响应非线性估计的叶片裂纹定位方法,并给出了系统非线性估计的定义和相应的计算方法。在叶片上多位置特征点采集非线性振动信号组成矩阵形式信号,再利用正交分解方法求解其线性近似矩阵信号,通过量化两者之间的误差得到叶片各点振动响应信号的非线性程度估计值。计算相同激励输入下健康状态和含裂纹叶片各点振动响应非线性度估计值的误差,寻找误差绝对值最大点实现裂纹的准确定位。通过在有限元软件中建立健康和含裂纹损伤的叶片有限元模型,在多种激励频率下进行瞬态动力学仿真验证,分析结果表明该方法具有很好的裂纹定位效果。     

风力机叶片原生缺陷转捩的能量释放机理研究

针对风力机叶片原生缺陷演化为裂纹进而扩展导致断裂的问题,分析细观缺陷在外载荷作用下转捩为宏观裂纹的能量释放定量关系明晰裂纹萌生机理。首先根据风力机叶片的载荷特点构造一个新的应力函数,基于正交异性复合材料基本公式求解原生缺陷层间开裂的应力强度因子、应力应变和位移分量,由此获得细观缺陷变形过程释放的塑性应变能;使用红外热像仪采集原生缺陷转捩过程的温度场并计算热能耗散量,基于不可逆热力学定律获得内储能随着疲劳周期的变化规律;最后,在万能试验机上对含有气泡和纤维断裂的叶片试件进行疲劳试验。结果表明,应用提出的应力函数的计算结果与试验结果误差较小,可作为细观缺陷变形时塑性应变能的计算依据。原生缺陷转捩为微小裂纹时,内储能的变化规律可作为判断缺陷类型和程度的依据。这项研究探索复合多层材料跨尺度的疲劳能量理论,有助于实现风电机组关键部件的全寿命周期监测。     

声发射技术在疲劳裂纹检测中的应用

主要讨论了声发射技术应用于疲劳裂纹的检测.根据疲劳裂纹的信号特征及测试场合,选用合适的疲劳裂纹检测方法,结合板簧疲劳裂纹试验机上的实验分析,得出声发射技术应用于疲劳裂纹检测的优势.     

基于声发射技术的金属高频疲劳监测

采用声发射技术监测高频疲劳条件下金属材料裂纹的扩展。介绍了如何运用软硬件处理的方法，从采集到的信号中分离出裂纹扩展的声发射信号。从处理后的声发射信号与观察得到的裂纹扩展对比来看，声发射参数的变化能够有效地反映材料疲劳裂纹扩展的过程，并且能更早地发现试样内部微小裂纹的变化。通过试验，得出了紧凑拉伸试样在裂纹稳定扩展阶段声发射信号能量率与应力强度因子幅值之间的关系式。     

Identification of fretting fatigue crack propagation mechanisms using acoustic emission

In this study, a fretting fatigue test has been equipped with an acoustic emission (AE) device in order to identify the successive crack propagation mechanisms. The fretting fatigue crack nucleation and propagation is a complicated process. Cracks initiate and propagate firstly due to shearing (mode II) and then by tension (mode I). The crack propagation generates mechanical energy emission. Elastic waves appear and can be detected through AE. A complete analysis of the AE signals (multi-parameter analysis, location of the AE in the loading cycle and a statistical analysis) led to an identification of three different steps in the crack propagation process. The evolution of the shearing and the tension influences in the crack propagation process is recognizable separately. Therefore, the three crack propagation steps have been identified as (a) crack propagation in mode II, (b) mixed mode crack propagation and (c) pure mode I crack propagation.     

On-line measurement of the size distribution of particles in a gas–solid two-phase flow through acoustic sensing and advanced signal analysis

Acoustic emission (AE) technology is a promising approach to non-intrusively measure the size distribution of particles in a pneumatic suspension. This paper presents an experimental study of the AE sensing technology coupled with signal processing algorithms for on-line particle sizing. The frequency characteristics of the AE signals under different experimental conditions are studied and compared. Initially, the characteristics of the background noise and AE signals are compared in the frequency domain for different air velocities and particle feeding rates. Through short-term energy analysis the working features of the suction unit and the vibration feeder are revealed. To find the effective characteristic frequency band of the AE signals, a multiple scanning and accumulation method assisted with a Savitzky–Golay smoothing filter is used to denoise the power spectra of the signals. Wavelet analysis is also deployed to denoise the signals. The denoising performance of different wavelet parameters (wavelet function, decomposition level and thresholding) is compared in terms of signal-to-noise ratio and signal smoothness. Finally, particle size is predicted through a neural network with energy fraction extracted through wavelet analysis. Experimental results demonstrate that the relative error of the particle sizing system is no greater than 23%.     

Determination of the damage mechanisms in armor structural materials via self-organizing map analysis

Dynamic compression fracture behaviors together with damage mechanisms of materials subjected to a compressive load at a high strain rate were studied by using a Self-organizing map (SOM). The materials used for the analysis were Al5083, Rolled homogeneous armor (RHA) and tungsten heavy alloy (WHA). The deformation behavior and Acoustic emission (AE) signal were acquired through a Split Hopkinson pressure bar (SHPB)-AE coupled test. The self-organization map which is one of the artificial neural network technique was employed to classify the AE energy, amplitude, and peak frequency according to the characteristics of the signal. In addition, distributions of AE signals were represented in stress-strain curves. The correlation between AE characteristics and material damage mechanisms was investigated. Based on the results, it was found that cluster 1 with high AE energy, high amplitude and low frequency was the cluster of the AE signal generated near the yield point of the material. Cluster 3, which has the opposite tendency, was confirmed as a cluster of AE signals that occurred just before a fracture of the material. The change in the measured value can be seen depending on the strain rate and the material, but the overall tendency was similar.     

基于声发射技术的点接触润滑状态研究

为研究点接触在部分弹流润滑条件下的润滑状态,使用球盘式摩擦磨损试验机进行试验,利用声发射技术监测不同工况下的润滑状态,并分析在不同工况下声发射信号特征参数的变化规律。结果表明:声发射信号特征参数计数、能量、信号强度对润滑状态的改变非常敏感,都随滑动速度的增加而减小,随载荷的增加而增加且在变化规律上几乎一致;利用声发射技术能够表征边界润滑向部分弹流润滑的过渡状态。     

Characteristics of elastic waves generated by fatigue crack penetration and growth in an aluminum plate

The characteristics of elastic waves emanated from crack initiation in 6061 aluminum alloy subjected to fatigue loading are investigated through experiments. The objective of the study is to determine the differences in the properties of the signals generated from fatigue test and also to examine if the sources of the waves could be identified from the temporal and spectral characteristics of the acoustic emission (AE) waveforms. The signals are recorded using nonresonant, flat, broadband transducers attached to the surface of the alloy specimens. The time dependence and power spectra of the signals recorded during the tests were examined and classified according to their special features. Six distinct types of signals were observed. The waveforms and their power spectra were found to be dependent on the crack propagation stage and the type of fracture associated with the signals. The potential application of the approach in health monitoring of structural components using a network of surface mounted broadband sensors is discussed.