[目的]针对当前国内外对散黄鸡蛋较难无损检测的问题,对散黄鸡蛋的振动特性和无损检测方法进行了研究。[方法]通过对鸡蛋进行生理学解剖,据此进行ANSYS有限元分析;构建基于磁致伸缩振子扫频式振动的鸡蛋散黄检测系统,对采集的鸡蛋振动...[目的]针对当前国内外对散黄鸡蛋较难无损检测的问题,对散黄鸡蛋的振动特性和无损检测方法进行了研究。[方法]通过对鸡蛋进行生理学解剖,据此进行ANSYS有限元分析;构建基于磁致伸缩振子扫频式振动的鸡蛋散黄检测系统,对采集的鸡蛋振动音频信号进行MUSIC(multiple signal classification)功率谱分析,再利用主成分分析法提取特征向量中的有用信息并构建基于对向传播神经网络(CPNN)的鸡蛋散黄检测模型。[结果]鸡蛋的ANSYS固液耦合有限元分析证明新鲜蛋与散黄蛋振动特性差异明显,为基于振动信息的鸡蛋散黄检测提供理论依据;基于磁致伸缩振子扫频式振动的鸡蛋散黄检测系统能有效增强鸡蛋振动信息,基于对向传播神经网络的鸡蛋散黄检测模型对300枚鸡蛋进行检测(训练集200枚,测试集100枚),结果新鲜蛋与散黄蛋的识别率分别达到98%和96%。[结论]新鲜蛋与散黄蛋振动存在差异,采用基于扫频振动式的MUSIC功率谱分析和对向传播神经网络的鸡蛋散黄检测是可行的。展开更多
The histories of differential pressure fluctuations and their Fast Fourier Transform spectrum have close relation with the flow regimes.Unfortunately,each type of flow regime is very difficult or impossible to be dist...The histories of differential pressure fluctuations and their Fast Fourier Transform spectrum have close relation with the flow regimes.Unfortunately,each type of flow regime is very difficult or impossible to be distinguished from the other on the basis of the fluctuations or the spectrum.The present paper provides a feasible solution, which the gas-liquid two-phase flow regimes can be recognized automatically and objectively on the basis of the combination of the Counter Propagation Network (CPN) and the FFT spectrum of the differential pressure fluctuations. The CPN takes advantages of simpler algorithm and fast training processes.Furthermore,it does not require a great deal of samples.The recognition possibility is determined by the clustering results of the Kohonen layer in the CPN.With the presented test cases,the possibility can be higher than 90 percent for different liquid phase velocity.展开更多
文摘[目的]针对当前国内外对散黄鸡蛋较难无损检测的问题,对散黄鸡蛋的振动特性和无损检测方法进行了研究。[方法]通过对鸡蛋进行生理学解剖,据此进行ANSYS有限元分析;构建基于磁致伸缩振子扫频式振动的鸡蛋散黄检测系统,对采集的鸡蛋振动音频信号进行MUSIC(multiple signal classification)功率谱分析,再利用主成分分析法提取特征向量中的有用信息并构建基于对向传播神经网络(CPNN)的鸡蛋散黄检测模型。[结果]鸡蛋的ANSYS固液耦合有限元分析证明新鲜蛋与散黄蛋振动特性差异明显,为基于振动信息的鸡蛋散黄检测提供理论依据;基于磁致伸缩振子扫频式振动的鸡蛋散黄检测系统能有效增强鸡蛋振动信息,基于对向传播神经网络的鸡蛋散黄检测模型对300枚鸡蛋进行检测(训练集200枚,测试集100枚),结果新鲜蛋与散黄蛋的识别率分别达到98%和96%。[结论]新鲜蛋与散黄蛋振动存在差异,采用基于扫频振动式的MUSIC功率谱分析和对向传播神经网络的鸡蛋散黄检测是可行的。
文摘The histories of differential pressure fluctuations and their Fast Fourier Transform spectrum have close relation with the flow regimes.Unfortunately,each type of flow regime is very difficult or impossible to be distinguished from the other on the basis of the fluctuations or the spectrum.The present paper provides a feasible solution, which the gas-liquid two-phase flow regimes can be recognized automatically and objectively on the basis of the combination of the Counter Propagation Network (CPN) and the FFT spectrum of the differential pressure fluctuations. The CPN takes advantages of simpler algorithm and fast training processes.Furthermore,it does not require a great deal of samples.The recognition possibility is determined by the clustering results of the Kohonen layer in the CPN.With the presented test cases,the possibility can be higher than 90 percent for different liquid phase velocity.
