Fault Classification of Hydroelectric Generating Unit Based on Improved Evidence Theory

2014 ◽  
Vol 7 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Jiatang Cheng ◽  
Li Ai ◽  
Zhimei Duan ◽  
Yan Xiong
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Fusion Method ◽  
Inconsistent Result ◽  
High Conflict

Aiming at the problem of the conventional vibration fault diagnosis technology with inconsistent result of a hydroelectric generating unit, an information fusion method was proposed based on the improved evidence theory. In this algorithm, the original evidence was amended by the credibility factor, and then the synthesis rule of standard evidence theory was utilized to carry out information fusion. The results show that the proposed method can obtain any definitive conclusion even if there is high conflict evidence in the synthesis evidence process, and may avoid the divergent phenomenon when the consistent evidence is fused, and is suitable for the fault classification of hydroelectric generating unit.

scholarly journals The Comprehensive Diagnostic Method Combining Rough Sets and Evidence Theory

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Guang Yang ◽  
Shuofeng Yu ◽  
Shan Lu ◽  
George Smith
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Rough Sets ◽  
Evidence Theory ◽  
Accuracy Rate ◽  
Complementary Information ◽  
High Conflict ◽  
Evidence Combination ◽  
Basic Probability ◽  
Inference Strategy

Abstract To solve the difficulties in practice caused by the subjectivity, relativity and evidence combination focus element explosion during the process of solving the uncertain problems of fault diagnosis with evidence theory, this paper proposes a fault diagnosis inference strategy by integrating rough sets with evidence theory along with the theories of information fusion and mete-synthesis. By using rough sets, redundancy of characteristic data is removed and the unrelated essential characteristics are extracted, the objective way of basic probability assignment is proposed, and an evidence synthetic method is put forward to solve high conflict evidence. The method put forward in this paper can improve the accuracy rate of fault diagnosis with the redundant and complementary information of various faults by synthesizing all evidences with the rule of the composition of evidence theory. Besides, this paper proves the feasibility and validity of experiments and the efficiency in improving fault diagnosis.

Application of Multi-Sensor Information Fusion Technology in the Fault Diagnosis of Transformer

2013 ◽  
Vol 385-386 ◽  
pp. 601-604
Author(s):  
Han Min Ye ◽  
Zun Ding Xiao
Keyword(s):  
Neural Networks ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
State Parameter ◽  
Accurate Diagnosis ◽  
Fusion Method ◽  
Fusion Technology ◽  
Sensor Information ◽  
Transformer Fault

The information fusion method is introduced into the transformer fault diagnosis. Through the sensor acquire transformer in operation of each state parameter, using two parallel BP neural networks to local diagnosis, with D-S evidence theory to global fuse the local diagnostic results. It realized the accurate diagnosis when transformer comes out one or a variety of faults at the same time. The experiments demonstrate that the credibility of diagnosis results are improved significantly, uncertainties are obviously reduced, which fully shows that the method is effective.

scholarly journals Research on Multi-element Fusion of Equipment Fault Monitoring Based on Evidence Theory

2020 ◽  
Vol 179 ◽  
pp. 02001
Author(s):  
Quan ZHOU ◽  
Lan LIU ◽  
Hao CHENG ◽  
Mian FU
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Health Monitoring ◽  
Evidence Theory ◽  
Reliable Data ◽  
Fusion Method ◽  
Relevant Evidence ◽  
Fault Monitoring ◽  
Different Sources

The multi-information fusion method of health monitoring based on evidence theory is used to study the problem of equipment fault diagnosis. The multi-information of fault monitoring is fused by the evidence theory and the reliability of the relevant evidence can be judged according to the ambiguity and uncertainty of the fault monitoring signal. Also it can determine the importance and reliability of the evidence from different sources. The data from multi-information fusion has higher reliability and accuracy which provides more reliable data for fault diagnosis.

scholarly journals Transformer graded fault diagnosis based on neighborhood rough set and XGBoost

2021 ◽  
Vol 243 ◽  
pp. 01002
Author(s):  
RuYan Guo ◽  
MinFang Peng ◽  
ZhenQi Cao ◽  
RunFu Zhou
Keyword(s):  
Fault Diagnosis ◽  
Rough Set ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fault Classification ◽  
Traditional Model ◽  
Source Information ◽  
Accuracy Rate ◽  
Neighborhood Rough Set ◽  
Diagnosis Model

Aiming at the uncertainty of fault type reasoning based on fault data in transformer fault diagnosis model, this paper proposed a hierarchical diagnosis model based on neighborhood rough set and XGBoost. The model used arctangent transformation to preprocess the DGA data, which could reduce the distribution span of data features and the complexity of model training. Using 5 characteristic gases and 16 gas ratios as the input characteristic parameters of the XGBoost model at all levels, reduction was performed on these 21 input feature attributes, features that had a high contribution to fault classification were retained, and redundant features were removed to improve the accuracy and efficiency of model prediction. Taking advantage of XGBoost's strong ability to extract a few features, the output of the model was the superposition of leaf node scores for each type of fault, the maximum score was the type of failure the sample belonged to, and its value was also the probability value. The obtained probability was used as one of the evidence sources to use D-S evidence theory for information fusion to verify the reliability of the model. Experiments have proved that the XGBoost graded diagnosis model proposed in this article has the highest overall accuracy rate comparing with the traditional model, reaching 93.01%, the accuracy of XGBoost models at all levels has reached more than 90%, the average accuracy rate is higher than that of the traditional model by an average of more than 2.7%, and the average time-consuming is only 0.0695 s. After D-S multi-source information fusion, the reliability of the prediction results of the model proposed in this paper has been improved.

A novel information fusion method based on Dempster-Shafer evidence theory for conflict resolution

2011 ◽  
Vol 15 (3) ◽  
pp. 399-411 ◽  
Author(s):  
Jianping Yang ◽  
Hong-Zhong Huang ◽  
Qiang Miao ◽  
Rui Sun
Keyword(s):  
Conflict Resolution ◽  
Information Fusion ◽  
Evidence Theory ◽  
Fusion Method

The Research on Fault Diagnosis of Civil Aircraft Information Fusion Based on the Evidence Theory

2014 ◽  
Vol 983 ◽  
pp. 392-395
Author(s):  
Xue Peng
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Evidence Theory ◽  
Similarity Coefficient ◽  
Maintenance Cost ◽  
Civil Aircraft ◽  
Fusion Theory ◽  
Improved Methods

In this paper, information fusion theory based on the evidence theory is used in the fault diagnosis field of civil aircraft. Considering the conflict resulted from information fusion in some certain conditions, two improved methods, including Similarity Coefficient and Full Factor are put forward to solve the conflict problems. In a nutshell, the methods are pretty effective and reliable, and the maintenance cost of airlines can be reduced obviously.

Research on Evaluation of Vehicle Dangerous Traveling State Based on Information Fusion Method

2013 ◽  
Vol 791-793 ◽  
pp. 1018-1022
Author(s):  
Peng Wang ◽  
Zhi Qiang Liu
Keyword(s):  
Information Fusion ◽  
Evaluation System ◽  
Evidence Theory ◽  
Recognition System ◽  
Fusion Method ◽  
Source Information ◽  
State Recognition ◽  
Research On Evaluation ◽  
Lane Departure ◽  
Level Information

An evaluation system of vehicle traveling state was proposed,and an unsafe vehicle traveling state recognition system was established using multi-level information fusion method. In view of the effects of the complexity of the driving environment, a variety of working conditions and the diversity of vehicle traveling characteristics, combing BP neural network with Dempster-Shafer evidence theory technique, the multi-information decision-level fusion was proposed to estimate the different kind model of the vehicle status. To verify the proposed strategies,the vehicle traveling posture evaluation system was established. The lane departure parameters and the relative distance parameters were studied in order to get the characterization of the vehicle traveling status information. The simulation results indicate that the adaptability and accuracy and the intelligence level of driving characterization estimation are significantly improved by using the pattern classification and decision technology of multi-source information fusion.

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