RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform
The RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of re...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2018/5851491 |
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| author | Guoqiang Cai Yaofei Wang Qiong Song Chen Yang |
| author_facet | Guoqiang Cai Yaofei Wang Qiong Song Chen Yang |
| author_sort | Guoqiang Cai |
| collection | DOAJ |
| description | The RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of relatively simple factual descriptions and statistical induction, failing to provide a comprehensive safety evaluation on the basis of system structure and accumulated data. In this paper, a new method of safety analysis is described for the failure mode of the air braking system, GO-Bayes. This method combines the structural modeling of the GO method with the probabilistic reasoning of Bayes methods, introduces the probability into the analysis process of GO, performs reliability analysis of the air braking system, and builds a big data platform for the air braking system to guide the system maintenance strategy. An automatic train air braking system is taken as an example to verify the usefulness and accuracy of the proposed method. Using ExtendSim software shows the feasibility of the method and its advantages in comparison with fault tree analysis. |
| format | Article |
| id | doaj-art-11cf8d94ad024f94b8c88e143a6904d0 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-11cf8d94ad024f94b8c88e143a6904d02025-02-03T01:20:37ZengWileyComplexity1076-27871099-05262018-01-01201810.1155/2018/58514915851491RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data PlatformGuoqiang Cai0Yaofei Wang1Qiong Song2Chen Yang3State Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, ChinaState Key Lab of Rail Traffic Control & Safety, Beijing Jiaotong University, Beijing 100044, ChinaInstitute of Computing Technologies, China Academy of Railway Sciences, Beijing 100081, ChinaThe RAMS (reliability, availability, maintainability, and security) of the air braking system is an important indicator to measure the safety performance of the system; it can reduce the life cycle cost (LCC) of the rail transit system. Existing safety analysis methods are limited to the level of relatively simple factual descriptions and statistical induction, failing to provide a comprehensive safety evaluation on the basis of system structure and accumulated data. In this paper, a new method of safety analysis is described for the failure mode of the air braking system, GO-Bayes. This method combines the structural modeling of the GO method with the probabilistic reasoning of Bayes methods, introduces the probability into the analysis process of GO, performs reliability analysis of the air braking system, and builds a big data platform for the air braking system to guide the system maintenance strategy. An automatic train air braking system is taken as an example to verify the usefulness and accuracy of the proposed method. Using ExtendSim software shows the feasibility of the method and its advantages in comparison with fault tree analysis.http://dx.doi.org/10.1155/2018/5851491 |
| spellingShingle | Guoqiang Cai Yaofei Wang Qiong Song Chen Yang RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform Complexity |
| title | RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform |
| title_full | RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform |
| title_fullStr | RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform |
| title_full_unstemmed | RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform |
| title_short | RAMS Analysis of Train Air Braking System Based on GO-Bayes Method and Big Data Platform |
| title_sort | rams analysis of train air braking system based on go bayes method and big data platform |
| url | http://dx.doi.org/10.1155/2018/5851491 |
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