基于VMD和BP神经网络的轨道病害诊断方法

针对从非线性、非稳态的轨枕振动信号中提取病害特征困难的问题,文中提出一种基于变分模态分解和多尺度排列熵的轨道病害特征提取方法,并采用BP神经网络病害诊断模型进行病害识别.利用变分模态分解方法将采集到的振动加速度信号进行分解,得到若干个本征模态分量.计算这些本征模态分量的多尺度排列熵值,将其作为轨道病害的高维特征向量,以...

Track Disease Diagnosis Method Based on VMD and BP Neural Network

In view of the problem of difficulty in extracting disease features from non-linear and unsteady sleeper vibration signals, this study proposes a track disease feature extraction method based on variational modal decomposition and multi-scale permutation entropy, and adopts the BP neural network disease diagnosis model to perform disease identification. The variational modal decomposition method is used to decompose the collected vibration acceleration signals to obtain several eigenmode components. The multi-scale permutation entropy value of these eigenmode components is calculated and used as the high-dimensional feature vector of the track disease to realize the noise reduction of the sleeper vibration signal and the extraction of the disease feature. Through the establishment of a BP neural network disease diagnosis model, high-dimensional feature vectors are input into the BP network for training, fitting, and verification, and compared with the method of combining empirical mode decomposition and BP neural network. The analysis results show that the proposed method has a higher recognition accuracy rate and can effectively diagnose disease.