Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train
In order to ensure the reliable monitoring of key components of train running gears and ensure safe and stable operation of a train, a full redundancy architecture of a running gear monitoring system for high-speed train was proposed. The scheme of “dual intelligent diagnosis unit + dual channel sen...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Control and Information Technology
2020-01-01
|
| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.014 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849224863242256384 |
|---|---|
| author | DONG Wei WANG Yunfei ZHANG Xiaoning ZHU Huilong |
| author_facet | DONG Wei WANG Yunfei ZHANG Xiaoning ZHU Huilong |
| author_sort | DONG Wei |
| collection | DOAJ |
| description | In order to ensure the reliable monitoring of key components of train running gears and ensure safe and stable operation of a train, a full redundancy architecture of a running gear monitoring system for high-speed train was proposed. The scheme of “dual intelligent diagnosis unit + dual channel sensor” is adopted in the architecture, which solves the problem of monitoring blind area caused by single diagnosis unit or single channel sensor failure. Based on the full redundancy architecture, a corresponding diagnosis model was proposed, according to the characteristics of ground and vehicle data, platform characteristics and application scenarios. The ground model is based on trend prediction; based on the model framework of “deep neural network+recurrent neural network”, the characteristic parameters of time series are extracted from massive historical data, and the early fault detection model is established. The onboard model is mainly based on real time diagnosis, and is easy to implement through the interactive judgment of dual channel waveform detection, the waveform consistency of dual channel sensors at vehicle level and train level can be diagnosed and identified in real time, combined with the practical application of a EMU of 350 km/h. The application results show that the full redundancy technology can reduce the risk of false alarm of sensor fault, improve the reliability of the diagnosis system, and ensure the stable and safe operation of the train. |
| format | Article |
| id | doaj-art-3ba73a8255b643f78a400d763e86aef8 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2020-01-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-3ba73a8255b643f78a400d763e86aef82025-08-25T06:50:27ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272020-01-01377782319537Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed TrainDONG WeiWANG YunfeiZHANG XiaoningZHU HuilongIn order to ensure the reliable monitoring of key components of train running gears and ensure safe and stable operation of a train, a full redundancy architecture of a running gear monitoring system for high-speed train was proposed. The scheme of “dual intelligent diagnosis unit + dual channel sensor” is adopted in the architecture, which solves the problem of monitoring blind area caused by single diagnosis unit or single channel sensor failure. Based on the full redundancy architecture, a corresponding diagnosis model was proposed, according to the characteristics of ground and vehicle data, platform characteristics and application scenarios. The ground model is based on trend prediction; based on the model framework of “deep neural network+recurrent neural network”, the characteristic parameters of time series are extracted from massive historical data, and the early fault detection model is established. The onboard model is mainly based on real time diagnosis, and is easy to implement through the interactive judgment of dual channel waveform detection, the waveform consistency of dual channel sensors at vehicle level and train level can be diagnosed and identified in real time, combined with the practical application of a EMU of 350 km/h. The application results show that the full redundancy technology can reduce the risk of false alarm of sensor fault, improve the reliability of the diagnosis system, and ensure the stable and safe operation of the train.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.014train running gear monitoringdata miningfull redundancytrain-ground integrationdiagnosis modeltrend predictiondeep neural networkrecurrent neural network |
| spellingShingle | DONG Wei WANG Yunfei ZHANG Xiaoning ZHU Huilong Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train Kongzhi Yu Xinxi Jishu train running gear monitoring data mining full redundancy train-ground integration diagnosis model trend prediction deep neural network recurrent neural network |
| title | Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train |
| title_full | Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train |
| title_fullStr | Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train |
| title_full_unstemmed | Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train |
| title_short | Research on Full Redundancy Technology Scheme of Running GearMonitoring System for High Speed Train |
| title_sort | research on full redundancy technology scheme of running gearmonitoring system for high speed train |
| topic | train running gear monitoring data mining full redundancy train-ground integration diagnosis model trend prediction deep neural network recurrent neural network |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2020.06.014 |
| work_keys_str_mv | AT dongwei researchonfullredundancytechnologyschemeofrunninggearmonitoringsystemforhighspeedtrain AT wangyunfei researchonfullredundancytechnologyschemeofrunninggearmonitoringsystemforhighspeedtrain AT zhangxiaoning researchonfullredundancytechnologyschemeofrunninggearmonitoringsystemforhighspeedtrain AT zhuhuilong researchonfullredundancytechnologyschemeofrunninggearmonitoringsystemforhighspeedtrain |