Yang Jinsong, Liu Jinzhao, Guo Jianfeng, Tao Kai
Infrastructure Inspection Research Institute, China Academy of Railway Sciences, Beijing 100081, China.
Sensors (Basel). 2024 Apr 30;24(9):2861. doi: 10.3390/s24092861.
Track smoothness has become an important factor in the safe operation of high-speed trains. In order to ensure the safety of high-speed operations, studies on track smoothness detection methods are constantly improving. This paper presents a track irregularity identification method based on CNN-Bi-LSTM and predicts track irregularity through car body acceleration detection, which is easy to collect and can be obtained by passenger trains, so the model proposed in this paper provides an idea for the development of track irregularity identification method based on conventional vehicles. The first step is construction of the data set required for model training. The model input is the car body acceleration detection sequence, and the output is the irregularity sequence of the same length. The fluctuation trend of the irregularity data is extracted by the HP filtering (Hodrick Prescott Filter) algorithm as the prediction target. The second is a prediction model based on the CNN-Bi-LSTM network, extracting features from the car body acceleration data and realizing the point-by-point prediction of irregularities. Meanwhile, this paper proposes an exponential weighted mean square error with priority inner fitting (EIF-MSE) as the loss function, improving the accuracy of big value data prediction, and reducing the risk of false alarms. In conclusion, the model is verified based on the simulation data and the real data measured by the high-speed railway comprehensive inspection train.
轨道平顺性已成为高速列车安全运行的重要因素。为确保高速运行安全，对轨道平顺性检测方法的研究不断改进。本文提出一种基于卷积神经网络-双向长短期记忆网络（CNN-Bi-LSTM）的轨道不平顺识别方法，并通过车体加速度检测来预测轨道不平顺，该检测易于采集且旅客列车即可获取，因此本文提出的模型为基于常规车辆的轨道不平顺识别方法发展提供了思路。第一步是构建模型训练所需的数据集。模型输入为车体加速度检测序列，输出为等长的不平顺序列。采用HP滤波（霍德里克-普雷斯科特滤波）算法提取不平顺数据的波动趋势作为预测目标。第二步是基于CNN-Bi-LSTM网络的预测模型，从车体加速度数据中提取特征并实现不平顺的逐点预测。同时，本文提出一种带优先内拟合的指数加权均方误差（EIF-MSE）作为损失函数，提高了大值数据预测的准确性，降低了误报风险。总之，基于高速铁路综合检测列车实测的仿真数据和实际数据对该模型进行了验证。
