Ren Junxiao, Jin Weidong, Wu Yunpu, Sun Zhang, Li Liang
School of Electrical Engineering, Southwest Jiaotong University, 999 Xi'an Road, Chengdu 611756, China.
China-ASEAN International Joint Laboratory of Integrated Transportation, Nanning University, 8 Longting Road, Nanning 541699, China.
Entropy (Basel). 2023 Apr 20;25(4):696. doi: 10.3390/e25040696.
The safe and comfortable operation of high-speed trains has attracted extensive attention. With the operation of the train, the performance of high-speed train bogie components inevitably degrades and eventually leads to failures. At present, it is a common method to achieve performance degradation estimation of bogie components by processing high-speed train vibration signals and analyzing the information contained in the signals. In the face of complex signals, the usage of information theory, such as information entropy, to achieve performance degradation estimations is not satisfactory, and recent studies have more often used deep learning methods instead of traditional methods, such as information theory or signal processing, to obtain higher estimation accuracy. However, current research is more focused on the estimation for a certain component of the bogie and does not consider the bogie as a whole system to accomplish the performance degradation estimation task for several key components at the same time. In this paper, based on soft parameter sharing multi-task deep learning, a multi-task and multi-scale convolutional neural network is proposed to realize performance degradation state estimations of key components of a high-speed train bogie. Firstly, the structure takes into account the multi-scale characteristics of high-speed train vibration signals and uses a multi-scale convolution structure to better extract the key features of the signal. Secondly, considering that the vibration signal of high-speed trains contains the information of all components, the soft parameter sharing method is adopted to realize feature sharing in the depth structure and improve the utilization of information. The effectiveness and superiority of the structure proposed by the experiment is a feasible scheme for improving the performance degradation estimation of a high-speed train bogie.
高速列车的安全舒适运行受到广泛关注。随着列车的运行，高速列车转向架部件的性能不可避免地会下降，最终导致故障。目前，通过处理高速列车振动信号并分析信号中包含的信息来实现转向架部件性能退化估计是一种常用方法。面对复杂信号，利用信息熵等信息理论来实现性能退化估计并不理想，近期研究更多地采用深度学习方法而非信息理论或信号处理等传统方法来获得更高的估计精度。然而，当前研究更多地聚焦于转向架某一特定部件的估计，并未将转向架视为一个整体系统来同时完成多个关键部件的性能退化估计任务。本文基于软参数共享多任务深度学习，提出一种多任务多尺度卷积神经网络，以实现高速列车转向架关键部件的性能退化状态估计。首先，该结构考虑了高速列车振动信号的多尺度特征，采用多尺度卷积结构更好地提取信号的关键特征。其次，考虑到高速列车的振动信号包含所有部件的信息，采用软参数共享方法在深度结构中实现特征共享，提高信息利用率。实验表明所提结构的有效性和优越性，是提高高速列车转向架性能退化估计的一种可行方案。
