scholarly journals Probabilistic Safety Analysis of High Speed and Conventional Lines Using Bayesian Networks

2016 ◽  
Author(s):  
Zacarias Grande Andrade ◽  
Enrique Castillo Ron ◽  
Alan O'Connor ◽  
Maria Nogal
Keyword(s):  
Bayesian Network ◽  
High Speed ◽  
Human Error ◽  
Driver Behavior ◽  
Safety Evaluation ◽  
Safety Analysis ◽  
Human Errors ◽  
Maintenance Costs ◽  
Railway Line ◽  
Probabilistic Safety

A Bayesian network approach is presented for probabilistic safety analysis (PSA) of railway lines. The idea consists of identifying and reproducing all the elements that the train encounters when circulating along a railway line, such as light and speed limit signals, tunnel or viaduct entries or exits, cuttings and embankments, acoustic sounds received in the cabin, curves, switches, etc. In addition, since the human error is very relevant for safety evaluation, the automatic train protection (ATP) systems and the driver behavior and its time evolution are modelled and taken into account to determine the probabilities of human errors. The nodes of the Bayesian network, their links and the associated probability tables are automatically constructed based on the line data that need to be carefully given. The conditional probability tables are reproduced by closed formulas, which facilitate the modelling and the sensitivity analysis. A sorted list of the most dangerous elements in the line is obtained, which permits making decisions about the line safety and programming maintenance operations in order to optimize them and reduce the maintenance costs substantially. The proposed methodology is illustrated by its application to several cases that include real lines such as the Palencia-Santander and the Dublin-Belfast lines.DOI: http://dx.doi.org/10.4995/CIT2016.2016.3428

Development of a time-variant causal model of human error in construction with dynamic Bayesian network

2019 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Zhangming Ma ◽  
Heap-Yih Chong ◽  
Pin-Chao Liao
Keyword(s):  
Bayesian Network ◽  
Human Error ◽  
Causal Model ◽  
Safety Management ◽  
Dynamic Bayesian Network ◽  
Human Errors ◽  
Content Type ◽  
Factors Affecting ◽  
Dynamic Causal Model

Purpose Human error is among the leading causes of construction-based accidents. Previous studies on the factors affecting human error are rather vague from the perspective of complex and changeable working environments. The purpose of this paper is to develop a dynamic causal model of human errors to improve safety management in the construction industry. A theoretical model is developed and tested through a case study. Design/methodology/approach First, the authors defined the causal relationship between construction and human errors based on the cognitive reliability and error analysis method (CREAM). A dynamic Bayesian network (DBN) was then developed by connecting time-variant causal relationships of human errors. Next, prediction, sensitivity analysis and diagnostic analysis of DBN were applied to demonstrate the function of this model. Finally, a case study of elevator installation was presented to verify the feasibility and applicability of the proposed approach in a construction work environment. Findings The results of the proposed model were closer to those of practice than previous static models, and the features of the systematization and dynamics are more efficient in adapting toward increasingly complex and changeable environments. Originality/value This research integrated CREAM as the theoretical foundation for a novel time-variant causal model of human errors in construction. Practically, this model highlights the hazards that potentially trigger human error occurrences, facilitating the implementation of proactive safety strategy and safety measures in advance.

scholarly journals Human reliability analysis of the Tehran research reactor using the SPAR-H method

2012 ◽  
Vol 27 (3) ◽  
pp. 319-332 ◽  
Author(s):  
Ramin Barati ◽  
Saeed Setayeshi
Keyword(s):  
Reliability Analysis ◽  
Research Reactor ◽  
Safety Analysis ◽  
Human Action ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Performance Shaping Factors ◽  
Tehran Research Reactor ◽  
Probabilistic Safety

The purpose of this paper is to cover human reliability analysis of the Tehran research reactor using an appropriate method for the representation of human failure probabilities. In the present work, the technique for human error rate prediction and standardized plant analysis risk-human reliability methods have been utilized to quantify different categories of human errors, applied extensively to nuclear power plants. Human reliability analysis is, indeed, an integral and significant part of probabilistic safety analysis studies, without it probabilistic safety analysis would not be a systematic and complete representation of actual plant risks. In addition, possible human errors in research reactors constitute a significant part of the associated risk of such installations and including them in a probabilistic safety analysis for such facilities is a complicated issue. Standardized plant analysis risk-human can be used to address these concerns; it is a well-documented and systematic human reliability analysis system with tables for human performance choices prepared in consultation with experts in the domain. In this method, performance shaping factors are selected via tables, human action dependencies are accounted for, and the method is well designed for the intended use. In this study, in consultations with reactor operators, human errors are identified and adequate performance shaping factors are assigned to produce proper human failure probabilities. Our importance analysis has revealed that human action contained in the possibility of an external object falling on the reactor core are the most significant human errors concerning the Tehran research reactor to be considered in reactor emergency operating procedures and operator training programs aimed at improving reactor safety.

A Markovian-Bayesian Network for Risk Analysis of High Speed and Conventional Railway Lines Integrating Human Errors

2015 ◽  
Vol 31 (3) ◽  
pp. 193-218 ◽  
Author(s):  
Enrique Castillo ◽  
Aida Calviño ◽  
Zacarías Grande ◽  
Santos Sánchez-Cambronero ◽  
Inmaculada Gallego ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Bayesian Network ◽  
High Speed ◽  
Human Errors

scholarly journals Human reliability analysis of high-temperature molten metal operation based on fuzzy CREAM and Bayesian network

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254861
Author(s):  
Yaju Wu ◽  
Kaili Xu ◽  
Ruojun Wang ◽  
Xiaohu Xu
Keyword(s):  
High Temperature ◽  
Bayesian Network ◽  
Molten Metal ◽  
Error Probability ◽  
Human Error ◽  
Human Reliability ◽  
Human Reliability Analysis ◽  
Human Errors ◽  
Metallurgy Industry ◽  
Human Error Probability

Human errors are considered to be the main causation factors of high-temperature molten metal accidents in metallurgical enterprises. The complex working environment of high- temperature molten metal in metallurgical enterprises has an important influence on the reliability of human behavior. A review of current human reliability techniques confirms that there is a lack of quantitative analysis of human errors in high-temperature molten metal operating environments. In this paper, a model was proposed to support the human reliability analysis of high-temperature molten metal operation in the metallurgy industry based on cognitive reliability and error analysis method (CREAM), fuzzy logic theory, and Bayesian network (BN). The comprehensive rules of common performance conditions in conventional CREAM approach were provided to evaluate various conditions for high-temperature molten metal operation in the metallurgy industry. This study adopted fuzzy CREAM to consider the uncertainties and used the BN to determine the control mode and calculate human error probability (HEP). The HEP for workers involved in high-temperature melting in steelmaking production process was calculated in a case with 13 operators being engaged in different high-temperature molten metal operations. The human error probability of two operators with different control modes was compared with the calculation result of basic CREAM, and the result showed that the method proposed in this paper is validated. This paper quantified point values of human error probability in high-temperature molten metal operation for the first time, which can be used as input in the risk evaluation of metallurgical industry.

Saskatchewan Highways and Transportation Reduced Tire Pressure Initiative

1997 ◽  
Vol 1589 (1) ◽  
pp. 1-8 ◽  
Author(s):  
Thomas L. Davies ◽  
Tami F. Wall ◽  
Allan Carpentier
Keyword(s):  
High Speed ◽  
Membrane Surface ◽  
Vehicle Stability ◽  
Road Maintenance ◽  
Long Term Effects ◽  
Tire Pressure ◽  
Reduced Pressure ◽  
Maintenance Costs ◽  
Short And Long Term

After examination of the research carried out by other agencies, Saskatchewan Highways and Transportation (SHT) embarked on an initiative to adapt low tire pressure technologies to the province's needs and environment. The focus of the initiative was to explore several technical questions from SHT's perspective: (a) Can low tire pressures be used to increase truck weights from secondary to primary without increasing road maintenance costs on thin membrane surface roads? (b) What are the short- and long-term effects of tire heating under high-speed/high-deflection constant reduced pressure (CRP) operations in a Saskatchewan environment? (c) What effects do lower tire pressures have on vehicle stability at highway speeds? To date, significant opportunities have been noted on local hauls (less than 30 min loaded at highway speeds) for CRP operation and long primary highway hauls that begin or end in relatively short secondary highway sections that limit vehicle weight allowed for the whole trip for central tire inflation technology. The background and environment for the initiative and the investigations and demonstrations envisioned and undertaken are briefly outlined.

scholarly journals Multiview deep learning based on tensor decomposition and its application in fault detection of overhead contact systems

2021 ◽  
Author(s):  
Xuewu Zhang ◽  
Yansheng Gong ◽  
Chen Qiao ◽  
Wenfeng Jing
Keyword(s):  
High Speed ◽  
Tensor Decomposition ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Training Time ◽  
Detection Model ◽  
Railway Line ◽  
Result Show ◽  
Deep Layers

AbstractThis article mainly focuses on the most common types of high-speed railways malfunctions in overhead contact systems, namely, unstressed droppers, foreign-body invasions, and pole number-plate malfunctions, to establish a deep-network detection model. By fusing the feature maps of the shallow and deep layers in the pretraining network, global and local features of the malfunction area are combined to enhance the network's ability of identifying small objects. Further, in order to share the fully connected layers of the pretraining network and reduce the complexity of the model, Tucker tensor decomposition is used to extract features from the fused-feature map. The operation greatly reduces training time. Through the detection of images collected on the Lanxin railway line, experiments result show that the proposed multiview Faster R-CNN based on tensor decomposition had lower miss probability and higher detection accuracy for the three types faults. Compared with object-detection methods YOLOv3, SSD, and the original Faster R-CNN, the average miss probability of the improved Faster R-CNN model in this paper is decreased by 37.83%, 51.27%, and 43.79%, respectively, and average detection accuracy is increased by 3.6%, 9.75%, and 5.9%, respectively.

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