Online monitoring data processing methods for railway slopes and its application: A case study of the Shuohuang Railway
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摘要:
基于北斗全球卫星导航系统的铁路边坡在线监测系统具有全天时、全天候、高精度和高可靠的特点，监测性能与数据处理模型密切相关。以朔黄（朔州—黄骅）铁路边坡变形在线监测系统为例，针对数据处理中涉及的数据预处理，噪声抑制和变形趋势预测三个环节开展研究。首先在数据预处理中采用3σ准则识别监测数据中的异常值并利用卡尔曼滤波算法对其进行修正，然后将CLEAN算法引入变形监测领域，利用CLEAN算法对监测数据进行噪声抑制，降低噪声分量对后续变形趋势预测的影响，最后利用RBF神经网络对噪声抑制后的数据建模分析，从而获得铁路边坡当前状态和未来变形趋势预测。工程应用表明，所提方法能够有效实现异常值检测及修正，噪声抑制性能良好，变形趋势预测精度高，应用效果较好。
Abstract:The online monitoring system for railway slopes, based on the Beidou global navigation satellite system, features all-weather, all-weather, high-precision, and high reliability. The effectiveness of system monitoring closely correlates with the data processing model. Taking the online monitoring system for slope deformation on the Shuohuang Railway as an example, this study focuses on three crucial aspects of data processing: data preprocessing, noise suppression, and deformation trend prediction. Initially, the 3σ criterion is employed for outlier detection in monitoring data, which is then corrected using the Kalman filter algorithm. Subsequently, the CLEAN algorithm, introduced to the field of deformation monitoring, is utilized to suppress noise, minimizing its impact on subsequent deformation trend predictions. Finally, an RBF neural network is applied for modeling and analyzing the noise-suppressed data to forecast current and future deformation trends of railway slopes. Engineering applications demonstrate that the proposed methods effectively detect and correct outliers, provide robust noise suppression, and yield precise deformation trend predictions, enhancing the practical application of monitoring systems.
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Key words:
- slope deformation /
- data processing /
- noise suppression /
- neural network /
- outlier detection
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表 1 不同方法预测结果对比
Table 1. Comparison of prediction results using different methods
GM（1,1） 小波变换 所提方法 MRE 0.68 0.27 0.12 RMSE 0.87 0.52 0.23 
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