scholarly journals Fault Inference of Electronic Equipment Based on Multi-State Fuzzy Bayesian Network

2019 ◽  
Vol 9 (20) ◽  
pp. 4248
Author(s):  
Ling Wang ◽  
Dongfang Zhou ◽  
Hao Zhang ◽  
Hui Tian ◽  
Caihong Zou ◽  
...  
Keyword(s):  
Bayesian Network ◽  
Electronic Equipment ◽  
Uncertain Information ◽  
Membership Degree ◽  
Heterodyne Receiver ◽  
Multiple Test ◽  
Fault State ◽  
Complex Relationships ◽  
Fault Inference ◽  
Fuzzy Interval

The aim of this study is to extend the directive function of fault inference in test and diagnosis system for electronic equipment. There are many problems, such as presence of various types of uncertain information in test set of electronic equipment, frequent degenerative faults, complex relationships of modules, multiple fault modes, existence of fuzzy interval in fault state, and interaction of each module. In view of these problems, the total membership degree of faults is commonly synthesized based on weights of multiple test indicators and normal membership degree of a single indicator. On this basis, this study builds the model for inferring fault states of leaf and root nodes based on multi-state triangular fuzzy Bayesian network (BN). Finally, this research carried out feasibility analysis on fault inference of a super-heterodyne receiver, thus verifying the efficiency and applicability of the method proposed in the study.

scholarly journals Complex q-Rung Orthopair Fuzzy Aggregation Operators and Their Applications in Multi-Attribute Group Decision Making

Information ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 5 ◽  
Author(s):  
Liu ◽  
Mahmood ◽  
Ali
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Imaginary Part ◽  
Group Decision Making ◽  
Group Decision ◽  
Aggregation Operators ◽  
Uncertain Information ◽  
Membership Degree ◽  
The Real ◽  
Complex Valued

In this manuscript, the notions of q-rung orthopair fuzzy sets (q-ROFSs) and complex fuzzy sets (CFSs) are combined is to propose the complex q-rung orthopair fuzzy sets (Cq-ROFSs) and their fundamental laws. The Cq-ROFSs are an important way to express uncertain information, and they are superior to the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets. Their eminent characteristic is that the sum of the qth power of the real part (similarly for imaginary part) of complex-valued membership degree and the qth power of the real part (similarly for imaginary part) of complex-valued non‐membership degree is equal to or less than 1, so the space of uncertain information they can describe is broader. Under these environments, we develop the score function, accuracy function and comparison method for two Cq-ROFNs. Based on Cq-ROFSs, some new aggregation operators are called complex q-rung orthopair fuzzy weighted averaging (Cq-ROFWA) and complex q-rung orthopair fuzzy weighted geometric (Cq-ROFWG) operators are investigated, and their properties are described. Further, based on proposed operators, we present a new method to deal with the multi‐attribute group decision making (MAGDM) problems under the environment of fuzzy set theory. Finally, we use some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.

scholarly journals Hypergraphs Based on Pythagorean Fuzzy Soft Model

2019 ◽  
Vol 24 (4) ◽  
pp. 100
Author(s):  
Gulfam Shahzadi ◽  
Muhammad Akram
Keyword(s):  
Score Function ◽  
Soft Set ◽  
Uncertain Information ◽  
Fuzzy Soft Set ◽  
Intuitionistic Fuzzy ◽  
Complex Relationships ◽  
Selection Of

A Pythagorean fuzzy soft set (PFSS) model is an extension of an intuitionistic fuzzy soft set (IFSS) model to deal with vague knowledge according to different parameters. The PFSS model is a more powerful tool for expressing uncertain information when making decisions and it relaxes the constraint of IFSS. Hypergraphs are helpful to handle the complex relationships among objects. Here, we apply the concept of PFSSs to hypergraphs, and present the notion of Pythagorean fuzzy soft hypergraphs (PFSHs). Further, we illustrate some operations on PFSHs. Moreover, we describe the regular PFSHs, perfectly regular PFSHs and perfectly irregular PFSHs. Finally, we consider the application of PFSHs for the selection of a team of workers for business and got the appropriate result by using score function.

scholarly journals Demand Prediction of Emergency Supplies under Fuzzy and Missing Partial Data

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Ming Zhang ◽  
Hanlin Wu ◽  
Zhifeng Qiu ◽  
Yifan Zhang ◽  
Boquan Li
Keyword(s):  
Prediction Model ◽  
Prediction Accuracy ◽  
Nearest Neighbor ◽  
Uncertain Information ◽  
K Nearest Neighbor ◽  
Emergency Rescue ◽  
Demand Prediction ◽  
Partial Data ◽  
Grey Relation ◽  
Fuzzy Interval

An accurate demand prediction of emergency supplies according to disaster information and historical data is an important research subject in emergency rescue. This study aims at improving supplies demand prediction accuracy under partial data fuzziness and missing. The main contributions of this study are summarized as follows. (1) In view that it is difficult for the turning point of the whitenization weight function to determine fuzzy data, two computational formulas solving “core” of fuzzy interval grey numbers were proposed, and the obtained “core” replaced primary fuzzy information so as to reach the goal of transforming uncertain information into certain information. (2) For partial data missing, the improved grey k-nearest neighbor (GKNN) algorithm was put forward based on grey relation degree and K-nearest neighbor (KNN) algorithm. Weights were introduced in the filling and logic test conditions were added after filling so that filling results were of higher truthfulness and accuracy. (3) The preprocessed data are input into the improved algorithm based on the genetic algorithm and BP neural networks (GABP) to obtain the demand prediction model. Finally the calculation presents that the prediction accuracy and its stability are improved at the five-group comparative tests of calculated examples of actual disasters. The experiments indicated that the supplies demand prediction model under data fuzziness and missing proposed in this study was of higher prediction accuracy.

Electronic Equipment Combination Fault Prediction Technology Research Based on LSSVM-HMM

2014 ◽  
Vol 687-691 ◽  
pp. 978-983
Author(s):  
Yan Ping Tian ◽  
Xiao Hui Ye ◽  
Ming Yin
Keyword(s):  
Failure Prediction ◽  
Prediction Method ◽  
Electronic Equipment ◽  
Condition Based Maintenance ◽  
Fault Prediction ◽  
Support Vector ◽  
Technology Research ◽  
State Prediction ◽  
Fault State ◽  
Fault Information

In order to solve the problem of complicated electronic equipment structure, inadequate fault information, hard to predict the fault and the existing failure prediction method cannot predict the state of the electronic equipment and other issues directly, we propose a combination failure prediction methods of least squares support vector machine (LSSVM) and hidden Markov model (HMM) based on Condition Based Maintenance (CBM). First, according to sensitivity analysis to determine the circuit elements to be changed to set the circuit by changing the parameters of the different components degraded state; secondly, create a combination failure prediction model; Finally, the circuit state prediction. The results show that the proposed method can directly predict the different states of the circuit, so as to realize the fault state prediction of the electronic equipment directly, the prediction accuracy can reach 93.3%.

scholarly journals Test optimization selection method based on NSGA-3 and improved Bayesian network model

2021 ◽  
Vol 39 (2) ◽  
pp. 414-422
Author(s):  
Lu Han ◽  
Xianjun Shi ◽  
Yuyao Zhai
Keyword(s):  
Bayesian Network ◽  
Network Model ◽  
Selection Method ◽  
Learning Ability ◽  
Uncertain Information ◽  
Bayesian Network Model ◽  
Test Selection ◽  
Large Solution ◽  
Selection Scheme ◽  
Test Optimization

Most of the solutions to existing test selection problems are based on single-objective optimization algorithms and multi-signal models, which maybe lead to some problems such as rough index calculation and large solution set limitations. To solve these problems, a test optimization selection method based on NSGA-3 algorithm and Bayesian network model is proposed. Firstly, the paper describes the improved Bayesian network model, expounds the method of model establishment, and introduces the model's learning ability and processing ability on uncertain information. According to the constraints and objective functions established by the design requirements, NSGA-3 is used to calculate the test optimization selection scheme based on the improved Bayesian network model. Taking a certain component of the missile airborne radar as an example, the fault detection rate and isolation rate are selected as constraints, and the false alarm rate, misdiagnosis rate, test cost, and test quantity are the optimization goals. The method of this paper is used for test optimization selection. It has been verified that this method can effectively solve the problem of multi-objective test selection, and has guiding significance for testability design.

Multiple criteria decision making based on Hamy mean operators under the environment of spherical fuzzy sets

2021 ◽  
pp. 1-26
Author(s):  
Muhammad Sarwar Sindhu ◽  
Tabasam Rashid ◽  
Agha Kashif
Keyword(s):  
Decision Making ◽  
Fuzzy Sets ◽  
Closed Interval ◽  
Multiple Criteria Decision Making ◽  
Score Function ◽  
Vital Role ◽  
Multiple Criteria ◽  
Uncertain Information ◽  
Membership Degree ◽  
Picture Fuzzy Sets

Aggregation operators are widely applied to accumulate the vague and uncertain information in these days. Hamy mean (HM) operators play a vital role to accumulate the information. HM operators give us a more general and stretchy approach to develop the connections between the arguments. Spherical fuzzy sets (SpFSs), the further extension of picture fuzzy sets (PcFSs) that handle the data in which square sum of membership degree (MD), non-membership degree (NMD) and neutral degree (ND) always lie between closed interval [0, 1]. In the present article, we modify the HM operators like spherical fuzzy HM (SpFHM) operator and weighted spherical fuzzy HM (WSpFHM) operator to accumulate the spherical fuzzy (SpF) information. Moreover, various properties and some particular cases of SpFHM and the WSpFHM operators are discussed in details. Also, to compare the results obtained from the HM operators a score function is developed. Based on WSpFHM operator and score function, a model for multiple criteria decision-making (MCDM) is established to resolve the MCDM problem. To check the significance and robustness of the result, a comparative analysis and sensitivity analysis is also performed.

scholarly journals Research on Fault Diagnosis for Pumping Station Based on T-S Fuzzy Fault Tree and Bayesian Network

2017 ◽  
Vol 2017 ◽  
pp. 1-7 ◽  
Author(s):  
Zhuqing Bi ◽  
Chenming Li ◽  
Xujie Li ◽  
Hongmin Gao
Keyword(s):  
Fault Diagnosis ◽  
Bayesian Network ◽  
Historical Data ◽  
Complex Structure ◽  
Fault Tree ◽  
Uncertain Information ◽  
New Approach ◽  
Pumping Station ◽  
The One ◽  
Tree Method

According to the characteristics of fault diagnosis for pumping station, such as the complex structure, multiple mappings, and numerous uncertainties, a new approach combining T-S fuzzy gate fault tree and Bayesian network (BN) is proposed. On the one hand, traditional fault tree method needs the logical relationship between events and probability value of events and can only represent the events with two states. T-S fuzzy gate fault tree method can solve these disadvantages but still has weaknesses in complex reasoning and only one-way reasoning. On the other hand, the BN is suitable for fault diagnosis of pumping station because of its powerful ability to deal with uncertain information. However, it is difficult to determine the structure and conditional probability tables of the BN. Therefore, the proposed method integrates the advantages of the two methods. Finally, the feasibility of the method is verified through a fault diagnosis model of the rotor in the pumping unit, the accuracy of the method is verified by comparing with the methods based on traditional Bayesian network and BP neural network, respectively, when the historical data is sufficient, and the results are more superior to the above two when the historical data is insufficient.

Study on Techniques of Modeling of Design for Testability

2013 ◽  
Vol 303-306 ◽  
pp. 394-398
Author(s):  
Zhen Sheng Wang ◽  
Jian Sun ◽  
Guang Yao Lian
Keyword(s):  
Bayesian Inference ◽  
Bayesian Network ◽  
Failure Modes ◽  
Design For Testability ◽  
Diagnostic Model ◽  
Uncertain Information ◽  
Electric Equipment

Due to the uncertainty in the testing process of electric equipment, Bayesian network is introduced to modify the hybrid diagnostic model, and the HD-BN model is established which has the ability to deal with uncertain information. Considering the relations between functions and failure modes, the priori probabilities of failure modes are corrected, and the uncertain matrix expressing relations between failure modes and tests under uncertainty is acquired based on Bayesian inference, which lay the foundation for further optimization of design for testability.

Application of Uncertain Information Fusion in Diagnosis Decision of Electronic Equipment

2013 ◽  
Vol 341-342 ◽  
pp. 715-718
Author(s):  
Jin Luo ◽  
Qi Bin Deng
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fuzzy Set ◽  
Fuzzy Theory ◽  
Electronic Equipment ◽  
Uncertain Information ◽  
Combination Rule ◽  
Fault Features ◽  
Diagnosis Method ◽  
Fuzzy Target

Focuses on how to dispose the multi-source uncertain information and promote the testability evaluation and fault diagnosis capability of the electronic equipment, this paper uses fuzzy theory in the uncertain information description and modeling. Based on the fuzzy set description of fuzzy target, new method is proposed to obtain fuzzy evidences from fuzzy fault features, and then, Dempster-Shafer combination rule are used to fuse multi-source fuzzy evidence to get diagnosis results. The proposed method of fuzzy evidence extraction can reduces uncertainties in fusion makings and improves fault identifications, and the fusion diagnosis method based on multi fuzzy evidence matching enhances the precision and reliability of the system fault diagnosis decision furthermore.

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