共查询到13条相似文献,搜索用时 46 毫秒
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在轴承的重载接触区域经常出现疲劳损伤,深入了解疲劳损伤的起因和发展对于预测轴承零件的寿命具有重大意义。选择用声发射法监测低温下接触疲劳试验中的裂纹起源和扩展过程,通过分析试验中的声发射信号即可确定显微疲劳的起源和扩展过程。 相似文献
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基于小波包和HHT变换的声发射信号分析方法 总被引:5,自引:1,他引:5
针对声发射管道泄漏检测过程中的噪声干扰问题,对基于小波包和经验模态分解(EMD)的声发射信号处理方法进行了研究.采用小波包分解算法和经验模态分解都可以对管道泄漏声发射信号进行分解,但分解结果却存在一定区别.EMD是近年来非平稳信号分析领域的一个突破,对管道泄漏声发射信号进行EMD分解后,选择包含声发射特征的若干固有模式函数(IMF分量)进行重构,可以提取到管道泄漏声发射信号的本质特征,消除噪声信号的干扰.相对小波包分解方法而言,对根据IMF分量重构的声发射信号进行相关分析计算,得到的管道泄漏点的位置更为精确. 相似文献
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根据法布里-珀罗干涉仪原理,设计一种新型的声发射光纤测量装置。可对高频(40kHz到数MHz)、小振幅(≤1nm)、频振高达数MHz的机械振动波实现在线检测。 相似文献
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针对声发射信号频散特性导致基于时延估计的气体管道泄漏定位误差大的问题,提出一种基于模态声发射时频分析的泄漏定位方法。该方法采用平滑伪Wigner-Ville时频分布对两泄漏信号的互相关函数进行时频分析,利用互相关函数的时频谱可同时提取泄漏信号的时间延迟和与之对应的频率;然后根据泄漏声发射信号的主导模态的频散曲线即可确定该频率对应的声速,利用实时确定的声速和时间延迟并根据两传感器之间的距离即可确定泄漏点的位置。实验结果表明,采用时频分析的气体管道泄漏定位误差与互相关相比减少了6倍。所提出的模态声发射时频定位方法能有效抑制泄漏信号的频散,提高泄漏信号的相关性,从而更适合用于声发射管道泄漏定位。 相似文献
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岩体损伤破坏过程三维定位声发射试验分析 总被引:1,自引:0,他引:1
岩石类材料的破坏是一种能量累积损伤过程,在力学上是宏观缺陷产生与扩展的累积过程。对完整岩石先进行了单轴压缩试验,在压缩试验的同时,使用声发射试验仪器记录下岩样的声发射参数。试验测试了不同岩性试样的损伤破坏过程,试验结果表明,岩石的应力和声发射变化率之间的关系具有三个阶段:不明显变化阶段、明显变化阶段、破坏前阶段。与此相对应,岩石声发射事件数和加载时间也具有三个阶段:声发射事件缓慢增长阶段、声发射事件稳定增长阶段、声发射事件高速增长阶段。在不同尺寸岩石的声发射试验中,发现不同尺寸对岩样的破坏形态影响不明显。尺寸较小的岩样进入声发射事件明显变化阶段的时间比尺寸较大的岩样早。 相似文献
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Srinivasa Pai T. N. Nagabhushana Raj B. K. N. Rao 《Machining Science and Technology》2013,17(4):653-676
The monitoring of tool wear is a most difficult task in the case of various metal-cutting processes. Artificial Neural Networks (ANN) has been used to estimate or classify certain wear parameters, using continuous acquisition of signals from multi-sensor systems. Most of the research has been concentrated on the use of supervised neural network types like multi-layer perceptron (MLP), using back-propagation algorithm and Radial Basis Function (RBF) network. In this article, a new constructive learning algorithm proposed by Fritzke, namely Growing Cell Structures (GCS) has been used for tool wear estimation in face milling operations, thereby monitoring the condition of the tool. GCS generates compact network architecture in less training time and performs well on new untrained data. The performance of this network has been compared with that of another constructive learning algorithm-based neural network, namely the Resource Allocation Network (RAN). For the sake of establishing the effectiveness of GCS, results obtained have been compared with those obtained using Multi Layer Perceptron (MLP), which is a standard and widely used neural network. 相似文献
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ZHANG Xinming HE Yongyong HAO Rujiang CHU Fulei State Key Laboratory of Tribology Tsinghua University Beijng China 《机械工程学报(英文版)》2007,20(2):104-108
Morlet wavelet is suitable to extract the impulse components of mechanical fault signals. And thus its continuous wavelet transform (CWT) has been successfully used in the field of fault diagnosis. The principle of scale selection in CWT is discussed. Based on genetic algorithm,an opti-mization strategy for the waveform parameters of the mother wavelet is proposed with wavelet en-tropy as the optimization target. Based on the optimized waveform parameters,the wavelet scalogram is used to analyze the simulated acoustic emission (AE) signal and real AE signal of rolling bearing. The results indicate that the proposed method is useful and efficient to improve the quality of CWT. 相似文献