共查询到19条相似文献,搜索用时 187 毫秒
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为了应对空间碎片的威胁,研制了一种基于声发射技术的用于实时监测空间碎片撞击航天器的在轨感知系统。对平面声发射源精确定位技术提出了需求。声发射信号属于非平稳随机信号,传统的小波变换无法充分获得其中携带的信息。利用HHT技术分析声发射信号波形,改进了AO模态到达时刻的确定算法,提高了线定位精度。在此基础上,将平面定位问题转化为求取函数最小值的优化问题,并利用单纯形法进行求解。在铝合金板上对铅芯折断波源进行了定位试验,结果表明,相对于小波变换,HHT更适于分析声发射信号;改进后的线定位方法和双时标法可有效应用于各向同性板的定位问题。研究结果为空间碎片在轨感知系统的研制提供了参考。 相似文献
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基于HHT的预应力钢筋混凝土梁断裂AE信号分析 总被引:2,自引:0,他引:2
针对混凝土材料损伤所产生的声发射信号复杂的特点,提出了基于Hilbert—Huang变换的分析混凝土声发射信号的新方法。利用全波形声发射技术记录了预应力混凝土梁在三点弯曲荷载试验下整个破坏过程的声发射信号,研究了声发射累计能量随时间变化的关系曲线,分析了梁在不同破坏阶段产生的实测声发射信号,获得了信号的Hilbert谱。通过与小波分析结果进行比较,显示出该方法具有处理精度高、自适应性强的特点,能有效地提取声发射信号中损伤的主要特征,为声发射信号处理提出了一种新的途径。 相似文献
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搅拌摩擦焊(friction stir welding, FSW)是一个多物理场耦合过程,焊接过程中声发射信号与焊接缺陷具有关联性. 基于声发射检测与多特征融合研究FSW缺陷监测方法,实时检测固态介质中的声发射信号,利用短时傅里叶变换、小波变换、梅尔频谱对声发射信号进行分析,确定焊接缺陷与声发射信号之间的相关性,最后通过concat融合方法构建多特征向量. 结果表明,FSW在预制缺陷处具有不同的声发射信号特征. 短时傅里叶、小波变换的主要频段集中在20 kHz,出现缺陷时功率分别达到?40,0.8 dB以上,梅尔频谱的主要频段集中在3.5 kHz出现缺陷时功率达到?40 dB以上. 应用多层神经网络分别建立基于单特征、多特征向量的焊接缺陷识别模型,多特征向量的焊接缺陷识别模型在数据集中的平均识别率达到97%,比基于单一特征缺陷识别模型提高18%. 研究的多特征缺陷识别模型能更准确地对焊接状态进行识别与监测. 相似文献
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《腐蚀科学与防护技术》2017,(4)
采用声发射和恒电位极化同步法研究了常压储罐底板钢Q235试样在pH值为4.5稀硫酸溶液中的均匀腐蚀行为,对声发射信号特性参数时域统计规律与底板钢均匀腐蚀过程进行关联分析,并利用时频分析技术基于Gabor小波变换提取腐蚀声发射信号的时频局部化特征。结果表明,底板钢均匀腐蚀自身及腐蚀产物的摩擦能够产生明显的声发射,声发射活度在一定程度上表征了腐蚀速率,这将为现场储罐底板声发射检测结果的解释和评定提供强有力的理论依据。 相似文献
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数字超声系统脉动噪声的估计与消除 总被引:1,自引:0,他引:1
采用高通滤波器和小波包变换技术对低碳钢摩擦焊接头进行超声无损检测,研究表明,高通滤波后,信噪比有所提高,但频率滤波器在滤除噪声的同时,也滤除了缺陷信息中所含的低频成分。小波包变换是一种比小波变换频率线性度更好的时频分析方法,能同时兼顾信号的陡变和缓变特征,从而清楚地区分时变信号和长时间的类周期信号。实验证明,小波包换能正地估计出脉动噪声,从而有效地去除之,提高信号的可观察性和信噪比。 相似文献
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小波变换在薄膜表面图像信号降噪中的应用 总被引:1,自引:0,他引:1
目的:当前,以原子力显微镜为代表的扫描探针显微镜设备可以获取纳米尺度薄膜样品的表面图像,但这些图像存在不同程度的噪声,影响图像质量和信息判断。为了更准确获取这些薄膜表面状态,需要对薄膜样品表面图像数据和信息进行降噪处理。方法结合AFM等设备成像特点以及小波变换的时频局域性特点,在介绍小波变换基本理论和噪声来源分析基础上,提出了一种多层小波分解去噪算法。传统的信号理论是建立在傅里叶变换基础上的,而傅里叶变换作为一种全局性的变化,其有一定的局限性,无法同时表述信号在时域和频域的局部性质,而这些局部特征恰恰是非平稳信号性质最关键的部分。小波变换保留了窗口傅里叶变换局部化的优点,改变了窗口傅里叶变换窗口函数大小固定的缺点。结果原始图像信号的频率在0 Hz到4000 Hz都有分布。通过小波变换后,信号波形更光滑,频谱在500 Hz到2000 Hz之间分布。结论将小波变换应用于薄膜表面图像信号降噪中,通过实验证明通过小波变换可以有效去除信号中的噪声部分。 相似文献
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简单介绍了连续和离散小波变换理论,结合CO2气体保护焊电弧电压信号的特点从理论上论证了小波变换方法在波控CO2焊中检测短路信号并给出短路控制信号的可行性.通过真、假短路信号及噪声信号的频谱分析,利用理论定性分析和数学公式的推导讨论了小波变换消除假短路信号和噪声信号干扰的机理.利用Matlab中的小波分析工具箱对实测所得的电弧电压离散数字信号采用不同的小波基进行小波变换分析,从分析中选出一套切实可行的小波变换数字滤波器组.最后在DSP(数字信号处理器)上编程实现短路信号的实时检测,验证了基于DSP的小波变换提高短路信号检测的实时性、准确性及可靠性. 相似文献
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A wavelet-based method is proposed to perform the analysis of NDE ultrasonic signals received during the inspection of reinforced composite materials. The non-homogenous nature of such materials induces a very high level of structural noise which greatly complicates the interpretation of the NDE signals. By combining the time domain and the classical Fourier analysis, the wavelet transform provides simultaneously spectral representation and temporal order of the signal decomposition components. To construct a C-scan image from the wavelet transform of the A-scan signals, we propose a selection process of the wavelet coefficients, followed by an interpretation procedure based on a windowing process in the time–frequency domain. The proposed NDE method is tested on cryogenic glass/epoxy hydrogen reservoir samples. 相似文献
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In recent years, the technique of wavelet transform has been applied widely in signal processing in different fields, including non-destructive testing of pile foundations. However, it was used mostly in signal filtering and the analysis of time-frequency diagram. This paper successfully utilized complex continuous wavelet transform to determine pile length and locations of defects on pile foundations by analyzing the time-frequency-phase angle diagram in different frequency band. Six piles with different types of defects were installed and tested to verify the proposed approach in this study. The results shows that complex continuous wavelet transform not only is able to provide high resolution results in different frequency bands, which is similar to that of continuous wavelet transform, but also simplifies the identification of the reflection of defects using 3D phase spectrogram. The location of defects can then be easily determined using phase diagram under the corresponding specific frequency. 相似文献
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Kunpeng Zhu Yoke San Wong Geok Soon Hong 《International Journal of Machine Tools and Manufacture》2009,49(7-8):537-553
This paper reviews the state-of-the-art of wavelet analysis for tool condition monitoring (TCM). Wavelet analysis has been the most important non-stationary signal processing tool today, and popular in machining sensor signal analysis. Based on the nature of monitored signals, wavelet approaches are introduced and the superiorities of wavelet analysis to Fourier methods are discussed for TCM. According to the multiresolution, sparsity and localization properties of wavelet transform, literatures are reviewed in five categories in TCM: time–frequency analysis of machining signal, signal denoising, feature extraction, singularity analysis for tool state estimation, and density estimation for tool wear classification. This review provides a comprehensive survey of the current work on wavelet approaches to TCM and also proposes two new prospects for future studies in this area. 相似文献
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Energy is an important physical variable in signal analysis. The distribution of energy with the change of time and frequency can show the characteristics of a signal. A time–energy density analysis approach based on wavelet transform is proposed in this paper. This method can analyze the energy distribution of signal with the change of time in different frequency bands. Simulation and practical application of the proposed method to roller bearing with faults show that the time–energy density analysis approach can extract the fault characteristics from vibration signal efficiently. 相似文献