SVDD-Based Research on Railway-Turnout Fault Detection and Health Assessment
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摘要: 为更加精准地提高现场道岔维护质量，减少设备故障，提出了基于支持向量描述（support vector domain description，SVDD）的道岔转换故障检测和健康指标评估方法.首先，通过对道岔设备的动作机理分析，将道岔功率曲线分为3个动作过程：解锁过程，转换过程，锁闭过程，每一过程分别提取道岔功率曲线相应的特征参数；然后，在此特征空间上，设计了基于SVDD的道岔故障检测算法和健康评估算法，从而实现对道岔设备的健康管理；最后，利用道岔系统数学模型仿真数据和现场采集的道岔真实监测数据，对提出的算法有效性进行验证.实验表明，用本文提出的健康指标检测道岔故障和评估道岔健康状态的准确率达95%以上，对现场道岔维护有很大指导意义.Abstract: In order to improve the quality of railway-turnout maintenance procedures and reduce equipment failure, a method for turnout-fault-detection and health-index evaluation based on the support vector domain description (SVDD) technique, is proposed. Through analysis of the switch-equipment mechanism, the turnout power curve was divided into 3 parts, each representing the unlocking, conversion, and locking processes respectively. To this end, characteristic parameters of the switch power curve were extracted. Subsequently, in the feature space, an SVDD-based turnout-fault-detection algorithm and health assessment were proposed. Lastly, the efficiency of the proposed algorithm was verified by comparing simulation results against experimental data. Experimental results demonstrate that the proposed health index possesses an accuracy of 95%, which could prove to be significant in on-site maintenance and guidance applications.
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Key words:
- railway turnouts /
- SVDD /
- fault detection /
- health evaluation
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图 1 道岔转换设备电流曲线和功率曲线采集原理
Figure 1. Data-collection principle for turnout current and power
图 2 道岔转换过程中的典型功率曲线
Figure 2. Typical power curves observed during switch-conversion process
图 4 道岔在“尖轨摩擦力变大”故障模式演变下的电流曲线
Figure 4. Turnout current under fault mode of switch under increased friction
图 6 道岔动作过程中健康指标和故障检测阈值
Figure 6. Health-index and fault-detection thresholds during switch conversion
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