基于动力学仿真数据的高速列车蛇行状态识别

近年来,人工智能技术广泛应用于高速列车运行状态的识别.用作列车运行状态识别的机器学习和深度学习模型需要大量的数据,然而在实测数据中获取的蛇行失稳数据样本少且不均衡,再者,由于实测数据获取成本高,实际采集数据困难,难以满足各种运行条件(稳定、小幅蛇行失稳和大幅蛇行失稳状态).为解决以上问题,通过SIMPACK软件建立高速...

Identification of high-speed trains hunting states based on dynamic simulation data

In recent years, artificial intelligence technology has been widely used to identify the operation states of high-speed trains. Machine learning and deep learning models for trains operation states recognition need a large amount of data, but the hunting instability data samples obtained from the measured data are small and unbalanced. Moreover, due to the high cost of acquiring the measured data, it is difficult to collect the actual data, and it is difficult to meet various operation states(stability, small amplitude hunting instability and large amplitude hunting instability). In order to solve the above problems, this paper establishes the high-speed train dynamics simulation model through SIMPACK software, and simulates various operation states data of the vehicle. Through mutual information analysis and Hilbert Huang transform(HHT) analysis, it is found that the simulation data are highly similar to the measured data in time domain and frequency domain. The simulation data is used as training set to train the artificial intelligence algorithm model of operation states recognition of high-speed trains, and then the measured data are used as test set for verification. The experimental results show that the simulation data can be used in both machine learning and deep learning models, which shows that simulation data can be used as training set in complex deep learning models.