首页> 外文期刊>Advances in Electrical and Computer Engineering >A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System Using Pattern Recognition and Image Processing Methods
【24h】

A New Contactless Fault Diagnosis Approach for Pantograph-Catenary System Using Pattern Recognition and Image Processing Methods

机译:基于模式识别和图像处理方法的受电弓系统的非接触故障诊断新方法

获取原文
           

摘要

Comfort and safety of railway transport has become more important as train speeds continue to increase. In electrified railways, the electrical current of the train is produced by the sliding contact between the pantograph and catenary. The quality of the current depends on the reliability of contact between the pantograph and catenary. So, pantograph inspection is very important task in electrified railways and it is periodically made for preventing dangerous situations. This inspection is operated manually by taking the pantograph to the service for visual anomalies. However, this monitoring is impractical because of time consuming and slowness, as locomotive remains disabled. An innovative method based on image processing and pattern recognition is proposed in this paper for online monitoring of the catenary-pantograph interaction. The images are acquired from a digital line-scan camera. Data are simultaneously processed according to edge detection and Hough transform, and then the obtained features are provided to a D-Markov based state machine, and the pantograph related faults, such as overheating of the pantograph strip, bursts of arcing, and irregular positioning of the contact line are diagnosed. The proposed method is verified by real faulty and healthy pantograph videos.
机译:随着火车速度的不断提高,铁路运输的舒适性和安全性变得越来越重要。在电气化铁路中,火车的电流是由受电弓和悬链线之间的滑动接触产生的。电流的质量取决于受电弓和悬链线之间接触的可靠性。因此,受电弓检查在电气化铁路中是非常重要的任务,并且定期进行以防止发生危险情况。通过将受电弓送往维修处进行视觉异常,可以手动进行此检查。但是,由于耗时且缓慢,因此这种监视是不切实际的,因为机车仍然被禁用。提出了一种基于图像处理和模式识别的创新方法,用于在线监测悬链受电弓的作用。图像是从数字线扫描相机获取的。根据边缘检测和霍夫变换同时处理数据,然后将获得的特征提供给基于D-Markov的状态机,以及与受电弓相关的故障,例如受电弓带过热,电弧爆炸以及不规则的定位。接触线被诊断。实际故障和健康的受电弓视频证明了该方法的有效性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号