Research on the new generation framework of PHM systems for railway trains
Scientific maintenance of operating vehicles and ensuring the safety of train operations have always been the core issues in the field of rail transit. In recent years, with the major demands of predictive maintenance and unmanned driving, there is an urgent need to realize the PHM (prognostics and...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2024-01-01
|
| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.001 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849323486903795712 |
|---|---|
| author | QIN Yong DING Ao WANG Biao LIU Han XU Lei CAI Changjun CHANG Zhenchen |
| author_facet | QIN Yong DING Ao WANG Biao LIU Han XU Lei CAI Changjun CHANG Zhenchen |
| author_sort | QIN Yong |
| collection | DOAJ |
| description | Scientific maintenance of operating vehicles and ensuring the safety of train operations have always been the core issues in the field of rail transit. In recent years, with the major demands of predictive maintenance and unmanned driving, there is an urgent need to realize the PHM (prognostics and health management) functions of holographic state perception, elaborate diagnosis and prediction, and timely feedback and disposal for railway trains. The existing system frameworks suffer from perception inefficiency, insufficient fusion, poor model optimization dynamics, weak computational synergy, and low levels of autonomous decision-making, etc. The development of advanced IoT, big data, artificial intelligence, digital twins, and other technologies has pushed the PHM system frameworks of railway trains to evolve to a higher level of intelligence. In this paper, the technical development stage of PHM systems of railway trains is reviewed and categorized. Furthermore, the PHM 4.0 framework for railway systems, which leverages ubiquitous sensing and collaborative computing, has been introduced. This framework is described in detail, elucidating its foundational principles, system structure, and key technologies. The paper also outlines the approaches, techniques, and anticipated outcomes associated with the deep integration of ubiquitous sensing, collaborative computing, and health management. Additionally, it clarifies the primary focus of current technological research, supporting enhanced safety and maintenance quality in railway operations. |
| format | Article |
| id | doaj-art-60a6f68ea3ee44c2aa7f67fbd4e608e5 |
| institution | Kabale University |
| issn | 1000-128X |
| language | zho |
| publishDate | 2024-01-01 |
| publisher | Editorial Department of Electric Drive for Locomotives |
| record_format | Article |
| series | 机车电传动 |
| spelling | doaj-art-60a6f68ea3ee44c2aa7f67fbd4e608e52025-08-20T03:49:02ZzhoEditorial Department of Electric Drive for Locomotives机车电传动1000-128X2024-01-0111050543956Research on the new generation framework of PHM systems for railway trainsQIN YongDING AoWANG BiaoLIU HanXU LeiCAI ChangjunCHANG ZhenchenScientific maintenance of operating vehicles and ensuring the safety of train operations have always been the core issues in the field of rail transit. In recent years, with the major demands of predictive maintenance and unmanned driving, there is an urgent need to realize the PHM (prognostics and health management) functions of holographic state perception, elaborate diagnosis and prediction, and timely feedback and disposal for railway trains. The existing system frameworks suffer from perception inefficiency, insufficient fusion, poor model optimization dynamics, weak computational synergy, and low levels of autonomous decision-making, etc. The development of advanced IoT, big data, artificial intelligence, digital twins, and other technologies has pushed the PHM system frameworks of railway trains to evolve to a higher level of intelligence. In this paper, the technical development stage of PHM systems of railway trains is reviewed and categorized. Furthermore, the PHM 4.0 framework for railway systems, which leverages ubiquitous sensing and collaborative computing, has been introduced. This framework is described in detail, elucidating its foundational principles, system structure, and key technologies. The paper also outlines the approaches, techniques, and anticipated outcomes associated with the deep integration of ubiquitous sensing, collaborative computing, and health management. Additionally, it clarifies the primary focus of current technological research, supporting enhanced safety and maintenance quality in railway operations.http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.001rail transitprognostics and health management 4.0 systems for railway trainsubiquitous sensingcollaborative computingintelligent operation and maintenanceunmanned driving |
| spellingShingle | QIN Yong DING Ao WANG Biao LIU Han XU Lei CAI Changjun CHANG Zhenchen Research on the new generation framework of PHM systems for railway trains 机车电传动 rail transit prognostics and health management 4.0 systems for railway trains ubiquitous sensing collaborative computing intelligent operation and maintenance unmanned driving |
| title | Research on the new generation framework of PHM systems for railway trains |
| title_full | Research on the new generation framework of PHM systems for railway trains |
| title_fullStr | Research on the new generation framework of PHM systems for railway trains |
| title_full_unstemmed | Research on the new generation framework of PHM systems for railway trains |
| title_short | Research on the new generation framework of PHM systems for railway trains |
| title_sort | research on the new generation framework of phm systems for railway trains |
| topic | rail transit prognostics and health management 4.0 systems for railway trains ubiquitous sensing collaborative computing intelligent operation and maintenance unmanned driving |
| url | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2024.01.001 |
| work_keys_str_mv | AT qinyong researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT dingao researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT wangbiao researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT liuhan researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT xulei researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT caichangjun researchonthenewgenerationframeworkofphmsystemsforrailwaytrains AT changzhenchen researchonthenewgenerationframeworkofphmsystemsforrailwaytrains |