针对分布式光纤周界安防系统中的振动入侵类型识别要求,提出一种基于时频特征的光纤振动模式识别方法。该方法以时频熵和具有时间关联性的重心频率为时频域特征,结合时域特征形成特征向量,并采用概率神经网络(PNN)进行多类模式识别。为...针对分布式光纤周界安防系统中的振动入侵类型识别要求,提出一种基于时频特征的光纤振动模式识别方法。该方法以时频熵和具有时间关联性的重心频率为时频域特征,结合时域特征形成特征向量,并采用概率神经网络(PNN)进行多类模式识别。为了不受不定原理的制约,时频域特征是基于经验模态分解(EMD)和Hi l ber t变换的,在实现上采用两级振动模式识别。实验结果表明,该方法能有效提高不同振动类型的识别准确率。展开更多
Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this s...Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.展开更多
文摘针对分布式光纤周界安防系统中的振动入侵类型识别要求,提出一种基于时频特征的光纤振动模式识别方法。该方法以时频熵和具有时间关联性的重心频率为时频域特征,结合时域特征形成特征向量,并采用概率神经网络(PNN)进行多类模式识别。为了不受不定原理的制约,时频域特征是基于经验模态分解(EMD)和Hi l ber t变换的,在实现上采用两级振动模式识别。实验结果表明,该方法能有效提高不同振动类型的识别准确率。
基金Project(51576213)supported by the National Natural Science Foundation of ChinaProject(2015RS4015)supported by the Hunan Scientific Program,ChinaProject(2016zzts323)supported by the Innovation Project of Central South University,China
文摘Gas–liquid two-phase flow abounds in industrial processes and facilities. Identification of its flow pattern plays an essential role in the field of multiphase flow measurement. A bluff body was introduced in this study to recognize gas–liquid flow patterns by inducing fluid oscillation that enlarged differences between each flow pattern. Experiments with air–water mixtures were carried out in horizontal pipelines at ambient temperature and atmospheric pressure. Differential pressure signals from the bluff-body wake were obtained in bubble, bubble/plug transitional, plug, slug, and annular flows. Utilizing the adaptive ensemble empirical mode decomposition method and the Hilbert transform, the time–frequency entropy S of the differential pressure signals was obtained. By combining S and other flow parameters, such as the volumetric void fraction β, the dryness x, the ratio of density φ and the modified fluid coefficient ψ, a new flow pattern map was constructed which adopted S（1–x）φ and （1–β）ψ as the vertical and horizontal coordinates, respectively. The overall rate of classification of the map was verified to be 92.9% by the experimental data. It provides an effective and simple solution to the gas–liquid flow pattern identification problems.
