Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach

Huang, Wencheng; Kou, Xingyi; Zhang, Yue; Mi, Rongwei; Yin, Dezhi; Xiao, Wei; Liu, Zhanru

doi:10.1016/j.ress.2020.107250

Cited by 27 publications

(9 citation statements)

References 23 publications

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“…Most methodological techniques were used for static analysis; however, BN could also be developed as a dynamic analysis model to deal with risk coupling problems. For example, Liu et al [21] constructed the dynamic BN to characterize the risk evolution in subsea blowout accidents, and Huang et al [22] quantified the effect of risk coupling between the operational failures using BN-K2 algorithm. In practice, clarifying the risk interaction process helps to maintain system safety and is critical for improving system reliability, especially for maritime transportation systems characterized by high uncertainty and human intervention.…”

Section: Literature Reviewmentioning

confidence: 99%

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Lan,

2024

Applied Sciences

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Seafarers’ unsafe acts as the direct causes of maritime accidents are considered to be the result of the interaction between complex and dynamic influencing factors. Identifying the risk evolution characteristics and paths of seafarers’ unsafe acts has always been a challenge in maritime safety management. For this purpose, the present study introduces association rule technique into complex network to develop a directed weighted interaction network of seafarers’ unsafe acts and their influencing factors. Through global network topology analysis and local network community detection, the risk evolution characteristics of seafarers’ unsafe acts in maritime accidents are analyzed from a multidimensional perspective. The results indicate that the developed network has small-world characteristics, and the top 10 critical nodes all belong to seafarers’ unsafe acts, of which failure to make proper sound and light signals achieves the highest PageRank value. Results from this study would help maritime stakeholders to understand the evolution mechanism of seafarers’ unsafe acts and develop safety management strategies for interrupting the risk propagation of seafarers’ unsafe acts.

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Section: Literature Reviewmentioning

confidence: 99%

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Lan,

2024

Applied Sciences

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“…The most two critical indicators are Outbreak Management Measures and Educational Attainment, whose sensitivity parameters are higher than 0.650. Epidemic management measures, mainly implemented to limit people's activity, will cause further community‐wide contact rate reduction (W. Huang et al., 2021; Teslya et al., 2020). The stricter government measures lead to the better epidemic prevention and control effect, which is in accordance with common sense.…”

Section: Application Of Nram and Sensitivity Analysismentioning

confidence: 99%

“…Based on the search‐score approach, K2 is an algorithm to search the correct or near‐optimal structure effectively (Benmohamed et al., 2022). Integrated with K2 algorithm, BN is more flexible to fit for the limited datasets (W. Huang et al., 2021). However, the sole BN_K2 algorithm cannot figure out the relationship among the numerous confounding factors.…”

Section: Introductionmentioning

confidence: 99%

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

et al. 2023

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COVID‐19 has caused a critical health concern and severe economic crisis worldwide. With multiple variants, the epidemic has triggered waves of mass transmission for nearly 3 years. In order to coordinate epidemic control and economic development, it is important to support decision‐making on precautions or prevention measures based on the risk analysis for different countries. This study proposes a national risk analysis model (NRAM) combining Bayesian network (BN) with other methods. The model is built and applied through three steps. (1) The key factors affecting the epidemic spreading are identified to form the nodes of BN. Then, each node can be assigned state values after data collection and analysis. (2) The model (NRAM) will be built through the determination of the structure and parameters of the network based on some integrated methods. (3) The model will be applied to scenario deduction and sensitivity analysis to support decision‐making in the context of COVID‐19. Through the comparison with other models, NRAM shows better performance in the assessment of spreading risk at different countries. Moreover, the model reveals that the higher education level and stricter government measures can achieve better epidemic prevention and control effects. This study provides a new insight into the prevention and control of COVID‐19 at the national level.

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“…This indicates that the probability of dam failure in earthen and rock ll dams is extremely high when multiple factors are coupled together, and therefore, we should try to avoid the occurrence of multi-factor coupling situations in the daily operation and maintenance management of earthen and rock ll dams. GeNIe software can measure the in uence strength of the parent node on the child nodes using the Euclidean distance formula and visualize the results (Huang et al 2021), as shown in Fig. 9.…”

Section: Bn Inference and Predictionmentioning

confidence: 99%

Dam failure risk analysis of earthen and rockfill dam systems: an approach based on a combination of an Interpreted Structural Model and a Bayesian Network with parameter learning

Li,

Zhang,

Wang

et al. 2023

Preprint

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The risk factors of earthen and rockfill dams during operation are characterized by uncertainty, complexity, interaction, etc. The coupling of risk factors can be more accurately identified in the process of dam risk analysis. To effectively analyze the interactions between the influencing factors within the system, this paper proposes a method for analyzing the risk of earthen and rockfill dam failure based on a combination of the Interpretive Structural Model (ISM) and Bayesian network (BN) model with the parameter learning. Meanwhile, the parameter learning of the BN model using the EM algorithm reduces the subjectivity of expert evaluation. In this paper, we analyzed the interrelationships among accidents by using the ISM method through statistics and analysis of actual accident cases. We established a hierarchical structure diagram including a five-level structure to derive the direct, indirect, and fundamental factors that lead to accidents. The EM algorithm was introduced to learn Bayesian network parameters, and the probability of occurrence of each influencing factor of earthen and rockfill dam failure was obtained through BN inference, diagnosis, and sensitivity analysis. The three most important influencing factors leading to earthen and rockfill dam failure were identified as flood overtopping, insufficient spillway discharge capacity, and damage to the spillway structure. A multi-factor coupling analysis was also conducted on the earthen and rockfill dams, and the results showed that the risk of dam failure was greatly increased as a result of the coupling between the influencing factors. In addition, we also found that management issues play an important role in earthen and rockfill dam failures and are key influencing factors that cannot be ignored. This method can be effectively applied to identify and analyze the influencing factors of earthen and rockfill dam failure in China.

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Operational failure analysis of high-speed electric multiple units: A Bayesian network-K2 algorithm-expectation maximization approach

Cited by 27 publications

References 23 publications

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

Risk Evolution Analysis of Seafarers’ Unsafe Acts in Maritime Accidents Based on Directed Weighted CN

A national risk analysis model (NRAM) for the assessment of COVID‐19 epidemic

Dam failure risk analysis of earthen and rockfill dam systems: an approach based on a combination of an Interpreted Structural Model and a Bayesian Network with parameter learning

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