基于时间序列数据挖掘的地铁车门亚健康状态识别方法

针对地铁门在开关过程出现的一些亚健康状态难以识别情况，提出一种基于时间序列数据挖掘的地铁车门亚健康状态识别的方法。该方法首先通过多尺度滑动窗口的方法并结合拓展符号聚集近似（ESAX）字符化算法对车门电机的转角、转速和电流数据进行字符化；然后计算其与车门正常运行状态下模板曲线之间的距离作为特征量，并使用主成分分析（PCA）法进行降维；最后结合基础特征利用分层模式识别模型对各类亚健康状态由粗到细逐层进行识别。以实测车门电机数据为例验证了所提方法的有效性。实验结果表明，该方法能够有效区分各类亚健康状态，识别率可达到99%。

Sub-health state identification method of subway door based on time series data mining

Aiming at the problem that the sub-health state of subway door is difficult to identify, a sub-health state identification method based on time series data mining was proposed. First of all, the angle, speed and current data of the subway door motor were discretized by combining multi-scale sliding window method and Extension of Symbolic Aggregate approXimation (ESAX) algorithm. And then, the features were obtained by calculating the distances among the templates under the normal state of the subway door, in which the Principal Component Analysis (PCA) was adopted to reduce feature dimension. Finally, combining with basic features, a hierarchical pattern recognition model was proposed to identify the sub-health state from coarse to fine. The real test data of subway door were taken as examples to verify the effectiveness of the proposed method. The experimental results show that the proposed method can recognize sub-health state effectively, and its recognition rate can reach 99%.