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Operational failure analysis of high-speed electric multiple units: A Bayesian network-k2 algorithm-expectation maximization approach

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2021年 205卷 107250-107250页

作者： Huang, Wencheng kou, Xingyi Zhang, Yue Mi, Rongwei Yin, Dezhi Xiao, Wei Liu, Zhanru Southwest Jiaotong Univ Sch Transportat & Logist China 611756 Sichuan Peoples R China Southwest Jiaotong Univ Inst Syst Sci & Engn Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl United Engn Lab Intergrated & Intelligent Tr Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl Engn Lab Integrated Transportat Big Data App Chengdu 611756 Sichuan Peoples R China China Railway Eryuan Engn Grp Co LTD China 610031 Sichuan Peoples R China

In this paper, a Bayesian Network-k2 algorithm-Expectation Maximization (BN-k2-EM) approach is proposed to quantify the intensity of coupling influence among the operational failures and find out the specific failure propagation chains in accidents of high-speed electric multiple units, quantitatively. k2 is applied to learn the BN structure because it can effectively learn the structure based on small scale data sets, EM is applied to learn the parameter in BN because it can accurately reflect the probability value among nodes with fast convergence speed. BN-k2-EM belongs to a data-driven method which overcomes the limitation of the logic-based BN approach. BN-k2-EM has 6 steps: establish the failures data matrix;determine the priority of nodes priority based on expanded average causal effect;learn the structure of BN based on k2 algorithm;learn the parameter of BN based on EM algorithm;causal reasoning and predication;reverse reasoning and diagnosis. Finally, a case study is conducted by using the historical high-speed EMU accidents happened in China from 2008 to 2019 as background. The influence strength and sensitivity of each failure mode in the case study are analyzed, and the sensitivity analysis of nodes in each system or sub-system is also conducted.

关键词： High-speed electric multiple units Operational failure analysis Bayesian network k2 algorithm Expectation maximization Failure propagation chain

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A Simple Node Ordering Method for the k2 algorithm based on the Factor Analysis 6

A Simple Node Ordering Method for the K2 Algorithm based on ...

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6th International Conference on Pattern Recognition Applications and Methods (ICPRAM)

作者： Tabar, Vahid Rezaei Allameh Tabatabai Univ Dept Stat Fac Math & Comp Sci Tehran Iran

ISBN: (纸本)9789897582226

In this paper, we use the Factor Analysis (FA) to determine the node ordering as an input for k2 algorithm in the task of learning Bayesian network structure. For this purpose, we use the communality concept in factor analysis. Communality indicates the proportion of each variable's variance that can be explained by the retained factors. This method is much easier than ordering-based approaches which do explore the ordering space. Because it depends only on the correlation matrix. As well, experimental results over benchmark networks 'Alarm' and 'Hailfinder' show that our new method has higher accuracy and better degree of data matching.

关键词： Bayesian Network Factor Analysis k2 algorithm Node Ordering Communality

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An efficient node ordering method using the conditional frequency for the k2 algorithm

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PATTERN RECOGNITION LETTERS 2014年第1期40卷 80-87页

作者： ko, Song kim, Dae-Won Chung Ang Univ Sch Comp Sci & Engn Seoul 156756 South Korea

In Bayesian networks, the k2 algorithm is one of the most effective structure-learning methods. However, because the performance of the k2 algorithm depends on node ordering, more effective node ordering inference methods are needed. In this paper, we therefore introduce a new node ordering algorithm based on a novel scoring function. Because a child has a better conditional frequency or probability under a correct parent than an incorrect one, we have designed a novel scoring function to evaluate this conditional frequency. Given two variables, our scoring function infers which is the better parent variable. Consequently, the proposed method infers candidate parents by considering all pairs of variables;it then uses these parents as input for the k2 algorithm. Experimental results indicate that our proposed method outperforms previous methods. (C) 2014 Elsevier B. V. All rights reserved.

关键词： Bayesian networks Causal relation Scoring function Node ordering k2 algorithm

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Learning qualitative probabilistic networks with reduced ambiguous signs from complete data

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INTERNATIONAL JOURNAL OF COMPUTING SCIENCE AND MATHEMATICS 2023年第4期18卷 392-404页

作者： Wang, Qian Xu, Wei Lv, Yali Wang, Lifang Taiyuan Univ Sci & Technol Sch Comp Sci & Technol Taiyuan 030024 Peoples R China Shanxi Univ Finance & Econ Sch Informat Taiyuan 030006 Peoples R China

Qualitative probabilistic networks (QPNs) are the qualitative abstractions of probabilistic networks or Bayesian networks, summarising probabilistic influences by qualitative signs. But the ambiguous sign is undesirable as it leads to uninformative results upon QPN inference. In this paper, we focus on learning QPN with reduced ambiguous signs from complete data. We first extend the definition of qualitative influences based on the situational signs, and prove the theorem on the range of qualitative signs. Second, we design an algorithm for learning QPN with reduced ambiguous (RAQPN algorithm). The QPN structure can be optimised by the k2 search algorithm, and the corresponding qualitative signs can be acquired by ordering the conditional probabilities that are represented by the frequency formats. Furthermore, when the ambiguous signs appear, they can be reduced by being given the situational state of the network. Finally, experiment results verify the validity and feasibility of the proposed methods.

关键词： qualitative probabilistic networks ambiguous signs Bayesian networks k2 algorithm

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Analysis and Forecast of Subway Construction Cost Based on Bayesian Network 12

Analysis and Forecast of Subway Construction Cost Based on B...

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12th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, IDAACS 2023

作者： Guo, Feng Niu, Donghui Zhang, Guoling Hou, Na Hu, Zhengbing China Railway Seventh Bureau Group Electrical Engineering Co. Ltd Zhengzhou450008 China National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9798350358056

In the process of subway construction, there is a complex nonlinear relationship among the engineering quantity, material cost, labor cost and total cost, which cannot be accurately calculated by manpower alone. Therefore, the reasonable deployment of human, material, capital and other resources in the future requires a high-precision and highly reliable analysis and prediction model. Aiming at this problem, this paper proposes a Bayesian network model of subway construction project cost prediction based on principal component analysis and k2 algorithm on the basis of analyzing the financial data, contract data and the existing engineering quantity calculation data in the information data system. Firstly, the principal component analysis method is used to screen the influencing factors of subway mechanical and electrical engineering and extract the core data features. Based on the extracted features, a Bayesian network model was built according to the principle of k2 algorithm to dynamically analyze the engineering cost, and the actual subway engineering cost data was used to train, verify and predict the model. The experimental results show that the accuracy of the prediction model proposed in this paper is 87.5%, and the prediction results are completely within the controllable range, thus proving the effectiveness of the model and realizing the dynamic tracking of the construction cost of electromechanical engineering. © 2023 IEEE.

关键词： Bayesian network k2 algorithm principal component analysis Subway cost prediction

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Improved Bayesian Network Structure Learning with Node Ordering via k2 algorithm

Improved Bayesian Network Structure Learning with Node Order...

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10th International Conference on Intelligent Computing (ICIC)

作者： Wei, Zhongqiang Xu, Hongzhe Li, Wen Gui, Xiaolin Wu, Xiaozhou Xi An Jiao Tong Univ Shaanxi Key Lab Comp Network Xian 710049 Peoples R China

ISBN: (纸本)9783319093390;9783319093383

The precise construction of Bayesian network classifier from database is an NP-hard problem and still one of the most exciting challenges. k2 algorithm can reduce search space effectively, improve learning efficiency, but it requires the initial node ordering as input, which is very limited by the absence of the priori information. On the other hand, search process of k2 algorithm uses a greedy search strategy and solutions are easy to fall into local optimization. In this paper, we present an improved Bayesian network structure learning with node ordering via k2 algorithm. This algorithm generates an effective node ordering as input based on conditional mutual information. The k2 algorithm is also improved combining with Simulated Annealing algorithm in order to avoid falling into the local optimization. Experimental results over two benchmark networks Asia and Alarm show that this new improved algorithm has higher classification accuracy and better degree of data matching.

关键词： Bayesian Network Classifier Structure Learning Search Strategy Conditional Mutual Information k2 algorithm

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Identifying vital nodes for yeast network by dynamic network entropy

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BMC BIOINFORMATICS 2024年第1期25卷 242页

作者： Liu, Jingchen Wang, Yan Men, Jiali Wang, Haohua Hainan Univ Sch Math & Stat Haikou 570228 Hainan Peoples R China Univ South China Affiliated Hosp 1 Dept Neurol Hengyang 421001 Hunan Peoples R China Hainan Univ Sch Life Sci Haikou 570228 Hainan Peoples R China Hainan Univ Key Lab Engn Modeling & Stat Computat Hainan Prov Haikou 570228 Hainan Peoples R China Shandong Univ Sch Math Jinan 250100 Shandong Peoples R China

BackgroundThe progress of the cell cycle of yeast involves the regulatory relationships between genes and the interactions proteins. However, it is still obscure which type of protein plays a decisive role in regulation and how to identify the vital nodes in the regulatory network. To elucidate the sensitive node or gene in the progression of yeast, here, we select 8 crucial regulatory factors from the yeast cell cycle to decipher a specific network and propose a simple mixed k2 algorithm to identify effectively the sensitive nodes and genes in the evolution of *** the multivariate of cell cycle data, we first utilize the k2 algorithm limited to the stationary interval for the time series segmentation to measure the scores for refining the specific network. After that, we employ the network entropy to effectively screen the obtained specific network, and simulate the gene expression data by a normal distribution approximation and the screened specific network by the partial least squares method. We can conclude that the robustness of the specific network screened by network entropy is better than that of the specific network with the determined relationship by comparing the obtained specific network with the determined relationship. Finally, we can determine that the node CDH1 has the highest score in the specific network through a sensitivity score calculated by network entropy implying the gene CDH1 is the most sensitive regulatory *** is clearly of great potential value to reconstruct and visualize gene regulatory networks according to gene databases for life activities. Here, we present an available algorithm to achieve the network reconstruction by measuring the network entropy and identifying the vital nodes in the specific nodes. The results indicate that inhibiting or enhancing the expression of CDH1 can maximize the inhibition or enhancement of the yeast cell cycle. Although our algorithm is simple, it is also the first

关键词： Network entropy Gene regulatory network k2 algorithm Partial least squares Network simulation Time series plateau interval

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Reliabilities analysis of evacuation on offshore platforms: A dynamic Bayesian Network model

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PROCESS SAFETY AND ENVIRONMENTAL PROTECTION 2021年 150卷 179-193页

作者： Wang, Yanfu Wang, kun Wang, Tao Li, Xi Yan khan, Fasial Yang, Zaili Wang, Jin China Univ Petr Coll Mech & Elect Engn Dept Safety Sci & Engn Qingdao Peoples R China Liverpool John Moores Univ Liverpool Logist Offshore & Marine LOOM Res Inst Liverpool Merseyside England Mem Univ Newfoundland Fac Engn & Appl Sci Ctr Risk Integr & Safety Engn C RISE St John NF A1B 3X5 Canada

An offshore platform is naturally vulnerable to accidents, such as the leakage of dangerous chemicals, fire and explosion. Oil and gas are explosive and all the equipment and pipes are squeezed into a limited area on a platform. Escape, Evacuation, and Rescue (EER) plans play a vital role as the last barrier to ensure the safety of personnel in the event of a major accident. As a result, the main contributors leading to evacuation failure need to be analyzed to prioritize technology development and select a robust EER strategy. This research aims to undertake the quantitative reliability analysis of various EER strategies on offshore platforms. First, a reliability prediction model of emergency evacuation is established for offshore platforms based on the k2 structure learning algorithm and a Bayesian network parameter learning method. The conditional probability table of each node is determined by combining the Bayesian estimation method and a junction tree reasoning engine. The reliability of emergency evacuation on a platform is predicted using a dynamic Bayesian network model. The transition probability is determined through a Markov method. The main factors leading to evacuation failure are investigated using the diagnostic reasoning method of Bayesian Network. The findings can provide insights for the development of cost effective EER strategies for an offshore platform. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： k2 algorithm Dynamic Bayesian network Reliability prediction of successful evacuation Analysis of influencing factors

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An investigation into the effect of geomechanical properties of reservoir rock on drilling parameters-a case study

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ARABIAN JOURNAL OF GEOSCIENCES 2021年第17期14卷 1-25页

作者： Delavar, Mohammad Reza Ramezanzadeh, Ahmad Tokhmechi, Behzad Shahrood Univ Technol Dept Min Petr & Geophys Engn Shahrood Iran

Drilling operation is among the most complex engineering activities as it is affected by a wide variety of factors. Many researchers have considered the effects of geomechanical parameters (GPs) of the reservoir rock on the drilling parameters (DPs), so that one can contribute to enhancing the drilling efficiency by building on the knowledge about the interaction between the GPs and the DPs. In this paper, artificial intelligence (AI) methods were used to investigate the association between wide range of the DPs and the GPs and provided new approach to improve drilling operations in carbonate reservoirs. Indeed, as aspect of the types of parameters and also machine learning methods, the study is novel method and seeks coverage in the lake of considering GPs in terms of drilling and its effectiveness. Dataset was collected from two oil wells located in the southwest of Iran. Firstly, 1-D geomechanical models were built to represent two reservoir formations, namely Asmari and Sarvak. Acknowledging the differences in resolution between the mud-logging and petrophysical data, the data was preprocessed (up-scaled) to have it prepared for further analysis. Subsequently, two methods were applied on the preprocessed data: (1) k2 algorithm as an instance of the so-called Bayesian network, showing both direct and indirect associations between the considered variables, and (2) multilayer perceptron (MLP) as an artificial neural network. The results of the k2 algorithm confirmed the direct impact of the unconfined compressive strength (UCS), a GP, and on the rate of penetration (ROP), a DP. It was further found that the internal friction angle and vertical effective stress (GPs) tend to affect the weight on bit (WOB) and torque (DPs). Focusing on the Asmari and Sarvak Formations, the results of the MLP implied that, as far as the effects of GPs were concerned, the rotational speed (RPM) was most greatly affected by the internal friction angle followed by the UCS. Comparative ch

关键词： Geomechanical properties Drilling parameters 1-D geomechanical model k2 algorithm Multilayer perceptron (MLP) Geomechanical unit (GMU)

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Risk Assessment and Prediction of Underground Utility Tunnels Based on Bayesian Network: A Case Study in Beijing, China

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JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2023年第6期32卷 2350096-2350096页

作者： Chen, Yongjun Li, Xiaojian Wang, Wenjuan Wu, Guangye Wang, Lulin Beijing Univ Civil Engn & Architecture Sch Urban Econ & Management Beijing 100044 Peoples R China Capital Univ Econ & Business Coll Business Adm 121 Zhangjialukou Beijing 10070 Peoples R China

The underground utility tunnels accommodate various types of urban lifelines, which are of great significance for improving the living standards of the citizens. With the rapid development of underground utility tunnels, the large-scale underground utility tunnel systems are gradually becoming the operational lifeblood of China's large cities. Currently, most of the underground utility tunnels' risks are estimated and analyzed from a static perspective, and the analysis results are one-sided. This study proposes a dynamic risk evaluation framework. A risk assessment and sensitivity analysis framework based on Bayesian network is established in this study. Combined with the groundwater and electric tunnel risk accident case study, the operation and maintenance data of Beijing Future Science and Technology City from 2010 to 2018 are collected for learning to obtain the conditional probability of the Bayesian network node by using the k2 algorithm. The overall evolution process from the beginning to the end of groundwater tunnel accidents is clearly described and displayed. Through sensitivity analysis and critical path analysis, the critical points of an accident and the probabilities of risk occurrence are identified and predicted. This proposed framework could facilitate the underground utility tunnel management for controlling risk resources, mitigating risk damage and reducing risk losses.

关键词： Underground utility tunnels risk factor identification Bayesian Network structure and parameter learning EM k2 algorithm

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