Fault information mining with causal network for railway transportation system

Liu, Jie; Xu, Yubo; Wang, Lisong

doi:10.1016/j.ress.2021.108281

Cited by 16 publications

(6 citation statements)

References 33 publications

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“…In particular, network-based methods have received much attention. For example, Lam and Tai [23] used network topological approach to identify complex risk interactions in railroad accidents, and Liu et al [24] also investigated related areas and used causal network method to mine fault information. Due to the high complexity of causal relationships among risk events, it is difficult for traditional methods to effectively analyze them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In general, the criticality of nodes depends on the network model and topology. Currently, scholars have proposed various node criticality assessment methods from different perspectives, such as centrality algorithm [24], K-Shell decomposition [45], and PageRank algorithm [46]. In particular, the PageRank algorithm is a specialized algorithm developed by Google to measure the importance of a specific page relative to other pages in a search engine, and is a classic method for identifying the criticality of nodes in a directed complex network [47].…”

Section: Node Criticalitymentioning

confidence: 99%

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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%

Section: Node Criticalitymentioning

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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“…This mitigates safety risks, lowers costs and even supports sustainable production in smart cities [ 21 , 22 ]. Many attempts have been made [ 23 , 24 , 25 ] to develop various statistical and machine learning solutions to predict component failures in a wide range of applications such as the following: manufacturing [ 26 , 27 ], automotive [ 28 , 29 , 30 , 31 ], and energy [ 32 ]. Prognostic models have been based on data collected from machines, social networks, and other sources to estimate the performance and condition of components or the number of upcoming warranty claims [ 33 ].…”

Section: Related Workmentioning

confidence: 99%

A Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

Khoshkangini

Tajgardan

Lundström

et al. 2023

Sensors

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Predicting breakdowns is becoming one of the main goals for vehicle manufacturers so as to better allocate resources, and to reduce costs and safety issues. At the core of the utilization of vehicle sensors is the fact that early detection of anomalies facilitates the prediction of potential breakdown issues, which, if otherwise undetected, could lead to breakdowns and warranty claims. However, the making of such predictions is too complex a challenge to solve using simple predictive models. The strength of heuristic optimization techniques in solving np-hard problems, and the recent success of ensemble approaches to various modeling problems, motivated us to investigate a hybrid optimization- and ensemble-based approach to tackle the complex task. In this study, we propose a snapshot-stacked ensemble deep neural network (SSED) approach to predict vehicle claims (in this study, we refer to a claim as being a breakdown or a fault) by considering vehicle operational life records. The approach includes three main modules: Data pre-processing, Dimensionality Reduction, and Ensemble Learning. The first module is developed to run a set of practices to integrate various sources of data, extract hidden information and segment the data into different time windows. In the second module, the most informative measurements to represent vehicle usage are selected through an adapted heuristic optimization approach. Finally, in the last module, the ensemble machine learning approach utilizes the selected measurements to map the vehicle usage to the breakdowns for the prediction. The proposed approach integrates, and uses, the following two sources of data, collected from thousands of heavy-duty trucks: Logged Vehicle Data (LVD) and Warranty Claim Data (WCD). The experimental results confirm the proposed system’s effectiveness in predicting vehicle breakdowns. By adapting the optimization and snapshot-stacked ensemble deep networks, we demonstrate how sensor data, in the form of vehicle usage history, contributes to claim predictions. The experimental evaluation of the system on other application domains also indicated the generality of the proposed approach.

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“…The data, once manual screening and identification are required, is undoubtedly a timeconsuming and laborious task, especially when the size is big. Therefore, considering the cause-effect relationship among variables, Liu et al [37]. proposed three unsupervised feature extraction methods to achieve failure information mining.…”

Section: Data Processing-keyword Extraction Of Text Datamentioning

confidence: 99%

The prediction of potential risk path in railway traffic events

Feng

et al. 2022

Reliability Engineering & System Safety

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Fault information mining with causal network for railway transportation system

Cited by 16 publications

References 33 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 Snapshot-Stacked Ensemble and Optimization Approach for Vehicle Breakdown Prediction

The prediction of potential risk path in railway traffic events

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