A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction f...A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction for the AE signals through wavelet packet transform for solving the current problems created by the presence of noise in AE signals and the existing problems in AE signal processing. The results show that the various characteristics of AE signals in coal rocks cracking under different situations can be clearly reflected, after the AE signals are de-noised by the wavelet packet. Compared to dry coal rocks, the number of AE occurrences in damp coal rocks was significantly reduced, as well as the average amplitude. The number of AE occurrences in damp and dry coal rocks clearly increased with increases in the loading rate, but the largest amplitude of the AE signals in damp coal rocks has been reduced. There is no clear evidence of change in dry coal rocks.展开更多
In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and ...In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring.展开更多
离心式压缩机叶片作为压缩机内最重要部件,长期承受周期性振动和流动诱使激励的作用。而叶片的故障将对压缩机的运行以及现场安全可靠性有严重的影响,因此如何有效地识别压缩机叶片裂纹早期故障显得尤为重要。由于叶片裂纹故障属于低频...离心式压缩机叶片作为压缩机内最重要部件,长期承受周期性振动和流动诱使激励的作用。而叶片的故障将对压缩机的运行以及现场安全可靠性有严重的影响,因此如何有效地识别压缩机叶片裂纹早期故障显得尤为重要。由于叶片裂纹故障属于低频微弱故障,通常被调制到叶片通过频率处,但是故障频率难以识别,清晰度较低。首先在叶片通过频率处进行信号滤波,然后应用Woods-Saxon and Gaussian Potential随机共振模型对特征频率进行加强,从而得到叶片裂纹故障频率。通过在叶片裂纹附近安装压力脉动传感器,利用压力脉动信号对叶片裂纹信息进行监测。实现模拟叶片裂纹的信号测试,验证了WSG随机共振模型在叶片裂纹早期故障识别中的有效性以及可靠性。同时通过应变试验进行验证此方法的有效性。展开更多
基金Financial support for this study, provided by the Key Basic Research Program of China (973) (No. 2007CB209407), is gratefully acknowledged
文摘A uniaxial load experiment on coal rocks at different stress rates was carried out, based on the characteristics of acoustic emission (AE) signals in cracking coal rocks, decomposition, de-noising and reconstruction for the AE signals through wavelet packet transform for solving the current problems created by the presence of noise in AE signals and the existing problems in AE signal processing. The results show that the various characteristics of AE signals in coal rocks cracking under different situations can be clearly reflected, after the AE signals are de-noised by the wavelet packet. Compared to dry coal rocks, the number of AE occurrences in damp coal rocks was significantly reduced, as well as the average amplitude. The number of AE occurrences in damp and dry coal rocks clearly increased with increases in the loading rate, but the largest amplitude of the AE signals in damp coal rocks has been reduced. There is no clear evidence of change in dry coal rocks.
文摘In recent years, the interest in damage identification of structural components through innovative techniques has grown significantly. Damage identification has always been a crucial concern in quality assessment and load capacity rating of infrastructure. In this regard, researchers focus on proposing efficient tools to identify the damages in early stages to prevent the sudden failure in structural components, ensuring the public safety and reducing the asset management costs. The sensing technologies along with the data analysis through various techniques and machine learning approaches have been the area of interest for these innovative techniques. The purpose of this research is to develop a robust method for automatic condition assessment of real-life concrete structures for the detection of relatively small cracks at early stages. A damage identification algorithm is proposed using the hybrid approaches to analyze the sensors data. The data obtained from transducers mounted on concrete beams under static loading in laboratory. These data are used as the input parameters. The method relies only on the measured time responses. After filtering and normalization of the data, the damage sensitive statistical features are extracted from the signals and used as the inputs of Self-Advising Support Vector Machine (SA-SVM) for the classification purpose in civil Engineering area. Finally, the results are compared with traditional methods to investigate the feasibility of the hybrid proposed algorithm. It is demonstrated that the presented method can reliably detect the crack in the structure and thereby enable the real-time infrastructure health monitoring.
文摘离心式压缩机叶片作为压缩机内最重要部件,长期承受周期性振动和流动诱使激励的作用。而叶片的故障将对压缩机的运行以及现场安全可靠性有严重的影响,因此如何有效地识别压缩机叶片裂纹早期故障显得尤为重要。由于叶片裂纹故障属于低频微弱故障,通常被调制到叶片通过频率处,但是故障频率难以识别,清晰度较低。首先在叶片通过频率处进行信号滤波,然后应用Woods-Saxon and Gaussian Potential随机共振模型对特征频率进行加强,从而得到叶片裂纹故障频率。通过在叶片裂纹附近安装压力脉动传感器,利用压力脉动信号对叶片裂纹信息进行监测。实现模拟叶片裂纹的信号测试,验证了WSG随机共振模型在叶片裂纹早期故障识别中的有效性以及可靠性。同时通过应变试验进行验证此方法的有效性。
