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Decision-making model for early diagnosis of congestive heart failure using rough set and decision tree approaches
2012
Journal of Biomedical Informatics
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(RSs) and decision trees. ...
The purpose of this study is to design a decision-making model that provides critical factors and knowledge associated with congestive heart failure (CHF) using an approach that makes use of rough sets ...
predictors in Table 6 , we constructed a decision-making model using the C5.0 decision tree model. ...
doi:10.1016/j.jbi.2012.04.013
pmid:22564550
fatcat:owp3ev2rg5fdpf5jtc7ioxmwzu
Failure diagnosis using decision trees
International Conference on Autonomic Computing, 2004. Proceedings.
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We present a decision tree learning approach to diagnosing failures in large Internet sites. ...
Our contributions include the statistical learning approach, the adaptation of decision trees to the context of failure diagnosis, and the deployment and evaluation of our tools on a high-volume production ...
Failure Diagnosis from Decision Tree Output Learning the decision tree is only half the battle. We also need to select the important features that correlate with the largest number of failures. ...
doi:10.1109/icac.2004.1301345
fatcat:xdyajt2h4bbvzfccbnlzgv6ese
A Hierarchical Modeling and Fault Diagnosis Technique for Complex Electronic Devices
2009
2009 IEEE Circuits and Systems International Conference on Testing and Diagnosis
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Multisignal model is used to describe the failure propagation relationship in electronic device system, and the most probable failure printed circuit boards (PCBs) can be found by Bayes inference. ...
Abstract⎯Due to the shortcomings of the diagnosis systems for complex electronic devices such as failure models hard to build and low fault isolation resolution, a new hierarchical modeling and diagnosis ...
Using the 20 group simulation failure data as SVM training data, the GA-SVM decision tree is generated, as shown in Fig. 4 and stored in database of the diagnosis system. ...
doi:10.1109/cas-ictd.2009.4960745
fatcat:sugsjgx6rzdpdkv7tkoqz36pgi
Rotating machinery fault diagnosis for imbalanced data based on decision tree and fast clustering algorithm
2017
Journal of Vibroengineering
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After that, decision tree is trained with the balanced fault sample set to get the fault diagnosis model. ...
Finally, gearbox fault data set and rolling bearing fault data set are used to test the fault diagnosis model. ...
The fast clustering algorithm is used to construct the balanced data set. Finally, decision tree is trained to get the fault diagnosis model. ...
doi:10.21595/jve.2017.18373
fatcat:d6gouzb4pvdvtgfxeqx74d4qfu
Detection of congestive heart failures using C4.5 Decision Tree
2013
Southeast Europe Journal of Soft Computing
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In this research, C4.5 decision tree algorithm was used for generating decision tree. ...
Decomposed ECG signals are used to detect different types of heart failures by using C4.5 decision tree classifier. (Goldberger, et al., 2000) . ...
doi:10.21533/scjournal.v2i2.32
fatcat:le4kqpkowfeeplwgudflyxurcy
A New Fault Diagnosis Method Based on Fault Tree and Bayesian Networks
2012
Energy Procedia
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This paper presents a novel method for diagnosing faults using fault tree analysis and Bayesian networks (BN) to optimize system diagnosis. ...
All minimal cut sets were generated via qualitative analysis of fault tree using an efficient zero-suppressed binary decision diagram (ZBDD), while the diagnostic importance factor (DIF) of components ...
decisions when trying to recover a system.
2.The Framework for Fault Diagnosis The method for fault diagnosis uses system fault tree model. ...
doi:10.1016/j.egypro.2012.02.255
fatcat:vabtt7hjmfc5phpq4nvxqnf4ra
Inter-Process Correlation Model based Hybrid Framework for Fault Diagnosis in Wireless Sensor Networks
2019
KSII Transactions on Internet and Information Systems
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Global decision tree, diagnoses critical failures due to errors in peer layer processes at network level. The proposed model has been analyzed using fault tree analysis. ...
The proposed model is realized through local and global decision trees for fault diagnosis. ...
Analysis code examines probe results to detect process failure and classify error level. Finally, fault diagnosis is performed through local decision tree (LDT) and global decision tree (GDT). ...
doi:10.3837/tiis.2019.02.004
fatcat:52fi7quoanbe3om2un77mrsvj4
A fault diagnosis method of engine rotor based on Random Forests
2016
2016 IEEE International Conference on Prognostics and Health Management (ICPHM)
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The frequency spectrum of the vibration signal of a rotor system is an important basis for rotor fault diagnosis, using the spectrum of rotor to build decision tree analysis is an important method for ...
As the single decision tree's anti-interference ability is very poor, this paper presents an engine rotor fault diagnosis method based on Random Forests. ...
The Random Forests is also based on decision tree, which has multiple trees to make a forest. For a single tree in the Random Forests, it uses the CART strategy to build a tree. ...
doi:10.1109/icphm.2016.7542838
dblp:conf/icphm/YaoWYSZ16
fatcat:go2bqztvsvbizgqj3bav24bazm
A Power Grid Fault Diagnosis Method based on Ensemble Decision Tree
2018
DEStech Transactions on Computer Science and Engineering
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multiple decision tree models vote together to determine the grid failure. ...
To solve this problem, this paper proposed a power grid fault diagnosis method based on ensemble decision tree (PGFD-EDT). ...
multiple decision tree models vote together to determine the grid failure. ...
doi:10.12783/dtcse/csae2017/17505
fatcat:7oya5xy4drcnhbrlud475557fa
Dynamic Diagnosis Strategy for Redundant Systems Based on Reliability Analysis and Sensors under Epistemic Uncertainty
2015
Journal of Sensors
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Specifically, it uses a dynamic fault tree to model dynamic fault modes and evaluates the failure rates of the basic events using fuzzy sets to deal with epistemic uncertainty. ...
A novel dynamic diagnosis strategy for complex systems is proposed to improve the diagnostic efficiency in the paper, which makes full use of dynamic fault tree, Bayesian networks (BN), fuzzy sets theory ...
To overcome these difficulties and limitations, Duan and Zhou proposed a new diagnosis method which used fuzzy sets to evaluate the failure rates of the basic events and used a dynamic fault tree model ...
doi:10.1155/2015/592142
fatcat:qv7lgovuhzc5rewtrspu3qxzdy
Quality and Reliability Data Fusion for Improving Decision Making by Means of Influence Diagram: Case Study
[chapter]
2019
Lecture Notes in Networks and Systems
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The diagnosis of failures is based on using Fault-Tree (FT) and Bayesian Network (BN). Firstly, a conversion from FT to BN is presented to establish a quick and accurate diagnosis. ...
Bayesian network (BN) model is developed and used as a decision-making tool. From this one, it is possible to quantify the probability of failure of this system. ...
Other techniques may be also used in fault diagnosis alongside BNs such as fault tree, fuzzy logic, and decision tree [2] . ...
doi:10.1007/978-3-030-23672-4_5
fatcat:kcibgxgw5bcfrpiauve4pnsqpm
A Novel Framework for Real-Time Fault Diagnosis Based on Dynamic Fault Tree Analysis
2013
Research Journal of Applied Sciences Engineering and Technology
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To meet the real-time diagnosis requirements of the complex system, this study proposes a novel framework for real-time fault diagnosis using dynamic fault tree analysis. ...
In terms of the challenge of model development, we use a dynamic fault tree model to capture the dynamic behavior of system failure mechanisms and calculate some reliability results by mapping a dynamic ...
PROPOSED REAL-TIME DIAGNOSIS SYSTEM FRAMEWORK The real-time diagnosis system uses the dynamic fault tree to model the complex system. ...
doi:10.19026/rjaset.5.4744
fatcat:sru5asdkgjcqrhec2zvtp3h77e
Fault Diagnosis of Wind Turbine's Gearbox Based on Improved GA Random Forest Classifier
2018
DEStech Transactions on Engineering and Technology Research
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And then, it used GA to optimize the number of decision trees and the number of attributes in the split attribute set in the random forest combined classifier. ...
In recent years, there were many studies on intelligent fault diagnosis of wind turbines based on vibration signals. There are also many algorithms for classification of failure categories. ...
The random forest combination classifier contains a plurality of randomly generated decision tree classifiers, and there is no association between the decision trees. ...
doi:10.12783/dtetr/amee2018/25323
fatcat:o3b37oes3jfu5ilphzduza25fu
HSFDONES: A Self-Leaning Ontology-Based Fault Diagnosis Expert System Framework
[chapter]
2011
IFIP Advances in Information and Communication Technology
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The fault diagnosis knowledge structure is defined and the relevant structure ontology and core fault ontology is researched in HSFDONES; the fault diagnosis data warehouse is built, the decision tree ...
HSFDONES is an expert system fault diagnosis which makes the fault diagnosis working more intelligently, HSFDONES uses the ontologybased self-leaning theory and technology to build fault diagnosis expert ...
represent a diagnosis result, the decision tree can display knowledge in a graphical way, the decision tree uses ID3 and the C4.5 as learning algorithm, ID3 is mainly applied to discrete attributes, C4.5 ...
doi:10.1007/978-3-642-18369-0_54
fatcat:24jmljpkurhjxerheoq74jmlru
Early Fault Diagnosis Model Design of Reciprocating Compressor Valve Based on Multiclass Support Vector Machine and Decision Tree
2022
Scientific Programming
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support vector machine and decision tree is designed. ...
Using the advantages of fast and efficient decision tree classification and the prominent characteristics of support vector machine in small sample binary classification, a multivariate classification ...
the model of the decision tree. ...
doi:10.1155/2022/7486271
fatcat:bzfhbvj5wzahtfqlmbeuwenaru
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