基于动态建模与重构的列车轴承故障检测和定位

刘强; 方彤; 董一凝; 秦泗钊

doi:10.16383/j.aas.c190247

基于动态建模与重构的列车轴承故障检测和定位

doi: 10.16383/j.aas.c190247

刘强^1,,
方彤^1,,
董一凝^2,,
秦泗钊^3,

1.
东北大学流程工业综合自动化国家重点实验室沈阳 110819, 中国
2.
美国斯坦福大学斯坦福 94305–2004, 美国
3.
美国南加州大学洛杉矶 90089, 美国

基金项目: 国家自然科学基金(61490704, 61991401, 61673097, 61573022), 中央高校基本科研业务费(N180802004, N160801001)资助

详细信息

作者简介:
刘强：东北大学教授. 主要研究方向为大数据建模、过程监控与故障诊断. 曾获辽宁省优秀博士学位论文奖, 中国自动化学会优秀博士学位论文提名奖. 本文通信作者. E-mail: liuq@mail.neu.edu.cn

方彤：东北大学硕士研究生. 主要研究方向为统计过程监控与故障诊断. E-mail: 1770520@stu.neu.edu.cn

董一凝：美国斯坦福大学博士后学者. 2011年毕业于清华大学电子工程系获学士学位. 2016年获美国南加州大学电子工程博士学位. 主要研究方向为过程数据分析, 潜变量模型, 统计过程监控和故障诊断. E-mail: yiningdo@stanford.edu

秦泗钊：美国南加州大学教授. IEEE Fellow, IFAC Fellow, AIChE Fellow. 主要研究方向为统计过程监控, 故障诊断, 模型预测控制, 系统辨识, 建筑能源优化与控制性能监控. E-mail: sqin@usc.edu

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出版历程
- 收稿日期: 2019-03-25
- 录用日期: 2019-07-17
- 刊出日期: 2019-12-01

Dynamic Modeling and Reconstruction Based Fault Detection and Location of Train Bearings

1.
State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110819, China
2.
Stanford University, Stanford CA 94305–2004, USA
3.
Viterbi School of Engineering, University of Southern California, Los Angeles CA 90089, USA

Funds: Supported by National Natural Science Foundation of China (61490704, 61991401, 61673097, 61573022) and Fundamental Research Funds for the Central Universities (N180802004, N160801001)

摘要

摘要: 列车运行时轴承故障的检测与定位对于列车运行安全与健康维护至关重要. 现有的轴承故障报警系统主要是基于单一轴温变量的规则诊断, 报警不及时. 针对上述问题, 本文结合运行于相似环境和速度的同车多轴轴温的相关性及轴温动态性, 提出了一种数据驱动的基于多轴轴温动态潜结构的列车轴承故障检测与定位方法. 首先, 提出基于动态内在典型相关分析(Dynamic-inner canonical correlation analysis, DiCCA)的列车多轴轴温动态潜结构建模方法; 其次, 利用所建立的模型, 提出基于DiCCA综合指标的列车轴承故障检测方法; 在此基础上, 提出基于DiCCA多向重构的列车轴承故障定位方法. 利用某列车实际运行时的轴温数据进行验证, 结果表明了所提方法的有效性.
- 故障检测 /
- 故障定位 /
- 动态潜结构建模 /
- 多方向重构
Abstract: The effective fault detection and diagnosis is necessary for operation safety and maintenance of the trains. The existing bearing alarm system normally applies rule-based method that cannot detect the fault into account before the bearing is heavily damaged. In this paper, taking the correlation and dynamic relation of multi-bearing temperatures, a data-driven dynamic latent structure based train bearing fault detection and diagnosis method is proposed. Firstly, a dynamic-inner canonical correlation analysis (DiCCA) based dynamic latent structure method is applied to extract the cross and auto dynamic relations within multi-dimensional bearing temperatures of the train. Secondly, a DiCCA based combined index is defined for fault detection of dynamic system and applied to detect the operational abnormality of the bearings. Thirdly, a DiCCA based multi-directional reconstruction method is proposed to locate the faulty bearing. Finally, application results using bearing temperature data collected from the practical operation of a train demonstrate the effectiveness of the proposed method.
- Fault detection /
- fault location /
- dynamic latent structrue modeling /
- multi-directional reconstruction

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图 1 列车轴温DiCCA模型中$ { v}_{k}$的自相关系数

Fig. 1 Autocorrelation coefficient of $ { v}_{k}$ in the DiCCA model of train bearings

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图 2 列车轴承故障检测及定位流程

Fig. 2 The process of fault detection and locating for train bearings

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图 3 DiCCA与列车系统诊断规则检测结果对比

Fig. 3 Fault detection result comparison of DiCCA and the rule-based method of the train system

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图 4 案例1的故障检测结果

Fig. 4 Fault detection result of Case 1

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图 8 案例5的故障检测结果

Fig. 8 Fault detection result of Case 5

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图 9 案例3基于$ \varphi_s$的MRBC贡献图

Fig. 9 $ \varphi_s$ based MRBC plot of Case 3

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图 10 案例3基于$ \varphi_v$的MRBC贡献图

Fig. 10 $ \varphi_v$ based MRBC plot of Case 3

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图 11 案例4基于$\varphi_s$的MRBC贡献图

Fig. 11 $\varphi_s$ based MRBC plot of Case 4

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图 12 案例4基于$\varphi_v$的MRBC贡献图

Fig. 12 $\varphi_v$ based MRBC plot of Case 4

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图 5 案例2的故障检测结果

Fig. 5 Fault detection result of Case 2

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图 6 案例3的故障检测结果

Fig. 6 Fault detection result of Case 3

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图 7 案例4的故障检测结果

Fig. 7 Fault detection result of Case 4

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表 1 基于规则的列车轴温预警及报警限

Table 1 Rule-based warning and alarm limits oftrain bearings

传感器位置	预警限($^{\circ} {\rm C}$)	报警限($^{\circ} {\rm C}$)
轴箱	100	120
齿轮箱	110	130
电机定子	160	180
电机传动端	110	130
电机非传动端	90	110

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表 2 各方法的轴承故障检测结果对比

Table 2 Result comparison among faultdetection methods

故障案例	开始检测到异常的样本点
故障案例	规则方法	PCA	DPCA	DiCCA
电机定子	9 000	8 982	8 235	7 324
电机非传动端	35 930	35 010	35 008	35 007
齿轮箱	19 340	11 042	11 039	11 027
轴箱	7 406	6 821	6 820	6 770
电机传动端	14 200	8 340	8 321	8 292

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