Deformation Recognition and Prediction of Track Slabs Based on Track Inspection Data
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摘要:
现有对高速铁路板式无砟轨道变形病害的检测效率不足，检测成本过高，而通过轨道动检数据能够一定程度上反映轨道板变形程度. 因此，搜集了CRTS Ⅰ、Ⅱ、Ⅲ型板线路3 a内的动检数据，引入小波能量作为轨道板变形评价指标，通过建立时空数据挖掘模型实现了不同轨道板的变形定位识别和劣化预测. 研究结果表明：受当地气温影响，轨道板变形程度具有一定的季节性规律，Ⅰ、Ⅱ型板在高温环境下出现翘曲或上拱，Ⅲ型板在低温环境下出现冻胀；3种轨道板中Ⅰ型板变形程度最小，Ⅱ型板最大，Ⅱ型板的残余变形会随时间累积，最终导致高低不平顺超限；长短期记忆网络能够实现对轨道板变形指标15～30 d内的短中期预测，Ⅰ型板变形的最佳预测结果R-square值接近0.9，而Ⅱ型板、Ⅲ型板变形的最佳预测R-square值均超过0.9.
Abstract:Existing methods for detecting deformation of high-speed railway slab ballastless tracksis low in efficiency and high in cost, while the track dynamic geometry inspection data can reflect the deformation level of slabs in a way.In this work, the track dynamic inspection data of railway lines of CRTS Ⅰ, Ⅱ, Ⅲ slabs within three years were collected and the wavelet energy was used as the deformation evaluation index of track slab. Finally, a temporal-spatial data mining model was proposed to realize the recognition and degradation prediction of track slabs. The results show that, affected by the local air temperature, the deformationlevel of track slabs has a seasonal pattern.Type-Ⅰ and type-Ⅱ slabs display warping and arching deformation in a high-temperature environment, while type-Ⅲ slab shows frost heavingin a low-temperature environment. Of the three types of track slabs, the deformation level of type-Ⅰ slab is mild, while the type-Ⅱ slab is most severe. The residual deformation of type-Ⅱ slab will accumulate over time, which may eventually lead to surface irregularity exceeding the limit. The long-term and short-term memory network can realize short- and mid-term prediction of the track slab deformation within 15 to 30 days. The the best predictionR-square value of the type-Ⅰ platedeformation is close to 0.9, while that of the type-Ⅱ and type-Ⅲ plate deformations exceeds 0.9.
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图 1 不同轨道板的变形特征波长统计结果
Figure 1. Statistical results of deformation characteristic wavelength for different track slabs
图 6 温度与各类轨道板变形相关性分析
Figure 6. Correlation analysis between temperature and deformation of different track slabs
表 1 工况设置参数
Table 1. Parameters of operation condition
参数 CRTS Ⅰ
型板CRTS Ⅱ
型板CRTS Ⅲ
型板结构跨数/个 20 20 20 结构长度/mm 4856 6450 5600 异常位置/m 124～172 329～393 554～610 正常幅值（最大
值）/mm0.2 0.2 0.2 异常幅值（最大
值）/mm0.6 0.6 0.6 
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表 2 不同轨道板预测结果的R-square值
Table 2. R-square values of prediction results for different track slabs
历史数据时间长度/d CRTS Ⅰ型板 CRTS Ⅱ型板 CRTS Ⅲ型板 15 d 30 d 45 d 15 d 30 d 45 d 15 d 30 d 45 d 15 0.85 0.66 0.61 0.92 0.81 0.76 0.92 0.84 0.64 30 0.88 0.75 0.57 0.91 0.82 0.74 0.93 0.85 0.65 45 0.88 0.75 0.67 0.91 0.80 0.76 0.93 0.86 0.74
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