Details
| Original language | English |
|---|---|
| Article number | 107675 |
| Journal | Engineering failure analysis |
| Volume | 154 |
| Early online date | 28 Sept 2023 |
| Publication status | Published - Dec 2023 |
Abstract
Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards.
Keywords
- Bayesian network, Failure probability, IFFTA, Preventive measures, Railway turnout, Train derailment
ASJC Scopus subject areas
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In: Engineering failure analysis, Vol. 154, 107675, 12.2023.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A failure probability assessment method for train derailments in railway yards based on IFFTA and NGBN
AU - Lai, Jun
AU - Wang, Kai
AU - Xu, Jingmang
AU - Wang, Ping
AU - Chen, Rong
AU - Wang, Shuguo
AU - Beer, Michael
N1 - Funding Information: The work was supported by the Chinese Scholarship Council (Grant No. 202207000077 ), National Natural Science Foundation of China (Grant No. 52122810 and 52108418 ), and Natural Science Foundation of Sichuan Province, China (Grant No. 2023NSFSC0398 ). The authors also thank the anonymous reviewers for their valuable comments that have improved the quality of this paper.
PY - 2023/12
Y1 - 2023/12
N2 - Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards.
AB - Derailment is one of the main hazards during train passes through railway turnouts (RTs) in classification yards. The complexity of the train-turnout system (TTS) and unfavorable operating conditions frequently cause freight wagons to derail at RTs. Secondary damages such as hazardous material spillage and train collisions can result in loss of life and property. Therefore, the primary goal is to assess the derailment risk and identify the root causes when trains pass through RTs in classification yards. To address this problem, this paper proposes a failure probability assessment approach that integrates intuitionistic fuzzy fault tree analysis (IFFTA) and Noisy or gate Bayesian network (NGBN) for quantifying the derailment risk at RTs. This method can handle the fact that the available information on the components of the TTS is imprecise, incomplete, and vague. The proposed methodology was tested through data analysis at Taiyuan North classification yard in China. The results demonstrate that the method can efficiently evaluate the derailment risk and identify key risk factors. To reduce the derailment risk at RTs and prevent secondary damage and injuries, measures such as optimizing turnout alignment, controlling impact between wagons, lubricating the rails, and regularly inspecting the turnout geometries can be implemented. By developing a risk-based model, this study connects theory with practice and provides insights that can help railway authorities better understand the impact of poor TTS conditions on train safety in classification yards.
KW - Bayesian network
KW - Failure probability
KW - IFFTA
KW - Preventive measures
KW - Railway turnout
KW - Train derailment
UR - http://www.scopus.com/inward/record.url?scp=85172731049&partnerID=8YFLogxK
U2 - 10.1016/j.engfailanal.2023.107675
DO - 10.1016/j.engfailanal.2023.107675
M3 - Article
AN - SCOPUS:85172731049
VL - 154
JO - Engineering failure analysis
JF - Engineering failure analysis
SN - 1350-6307
M1 - 107675
ER -