Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (3): 905-910.DOI: 10.11772/j.issn.1001-9081.2017081912

Previous Articles     Next Articles

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

XUE Yu1, MEI Xue1, ZHI Youran2, XU Zhixing2, SHI Xiang2   

  1. 1. College of Electrical Engineering and Control Science, Nanjing Tech University, Nanjing Jiangsu 211816, China;
    2. Nanjing Kangni Mechanical & Electrical Company Limited, Nanjing Jiangsu 210013, China
  • Received:2017-08-04 Revised:2017-09-04 Online:2018-03-10 Published:2018-03-07


薛钰1, 梅雪1, 支有冉2, 许志兴2, 史翔2   

  1. 1. 南京工业大学 电气工程与控制科学学院, 南京 211816;
    2. 南京康尼机电股份有限公司, 南京 210013
  • 通讯作者: 梅雪
  • 作者简介:薛钰(1992-),男,江苏扬州人,硕士研究生,主要研究方向:数据挖掘、模式识别;梅雪(1975-),女,内蒙古呼伦贝尔人,副教授,博士,主要研究方向:图像处理、模式识别;支有冉(1984-),男,江苏徐州人,副教授,博士,主要研究方向:火灾科学、模式识别;许志兴(1970-),男,江苏南京人,博士,主要研究方向:故障诊断、数据挖掘;史翔(1956-),男,江苏南京人,硕士,主要研究方向:故障诊断、数据挖掘。

Abstract: 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%.

Key words: time series data mining, engine parameter of train door, pattern recognition, Principal Component Analysis (PCA), Extension of Symbolic Aggregate approXimation (ESAX)

摘要: 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%。

关键词: 时间序列数据挖掘, 地铁门电机数据, 模式识别, 主成分分析, 拓展符号聚集近似

CLC Number: