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.

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 Composite Fault Diagnosis for Rotating Machinery of Large Units Based on Evidence Theory and Multi-Information Fusion

2019 ◽  
Vol 2019 ◽  
pp. 1-12 ◽  
Author(s):  
Naiquang Su ◽  
Xiao Li ◽  
Qinghua Zhang ◽  
Zhiqiang Huo ◽  
Xavier Chiementin
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Large Scale ◽  
Evidence Theory ◽  
Rotating Machinery ◽  
Uncertain Information ◽  
Sample Distribution ◽  
Multiple Data ◽  
Diagnosis Method ◽  
Conflict Information

Due to the complexity of the structure and process of large-scale petrochemical equipment, different fault characteristics are mixed and present multiple couplings and ambiguities, leading to the difficulty in identifying composite faults in rotating machinery. This paper proposes a composite faults diagnosis method for rotating machinery of the large unit based on evidence theory and multi-information fusion. The evidence theory and multi-information fusion method mainly deal with multisource information and conflict information, synthesize multiple uncertain information, and obtain synthetic information from multiple data sources. To detect faults in rotating machinery, the dimensionless index ranges of composite faults are first used to form a feature set as the reference. Then, a two-sample distribution test is applied to compare the known fault samples with the tested fault samples, and the maximum statistical distance is used. Finally, the multiple maximum statistical distances are fused by evidence theory and identifying fault types based on the fusion result. The proposed method was applied to the large petrochemical unit simulation experiment system, the results of which showed that our proposed method could accurately identify composite faults and provide maintenance guidance for composite fault diagnosis.

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dengji Zhou ◽  
Tingting Wei ◽  
Huisheng Zhang ◽  
Shixi Ma ◽  
Fang Wei
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Information Fusion ◽  
Evidence Theory ◽  
Diagnostic System ◽  
Fusion Model ◽  
First Case ◽  
Evidence Algorithm ◽  
Data Layer ◽  
Analytical System

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved Dempster–Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

Research on Acoustic Monitoring for Boiler Tube Leakage Based on Information Fusion Theory

2013 ◽  
Vol 760-762 ◽  
pp. 886-890
Author(s):  
Peng Tong ◽  
Chun Jing Geng
Keyword(s):  
Neural Network ◽  
Information Fusion ◽  
Initial Phase ◽  
Evidence Theory ◽  
Acoustic Monitoring ◽  
Boiler Tube ◽  
Monitoring Method ◽  
Fusion Theory

The problem of how to detect and diagnose the tube leakage and blast through the methods of acoustics and information fusion is dealt with in this paper with the purpose of detecting the accident more accurately at its initial phase. Firstly, the acoustic monitoring method is employed since it is contactless, and then the weak leakage is detected, analyzed and diagnosed through such methods as the PCA, neural network and D-S evidence theory. Secondly, the simulation is conducted, which testifies that the diagnosis effect can be improved greatly by this way.

Approach of Combination of Weighted Evidence Based on Evidential Closeness Degree and its Application

2012 ◽  
Vol 630 ◽  
pp. 377-382
Author(s):  
Zhuan Zhe Zhao ◽  
Min Ping Jia ◽  
Kang He ◽  
Hao Zhou ◽  
Yu Jie Ding
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Calculation Method ◽  
Evidence Theory ◽  
Weights Of Evidence ◽  
Evidence Based ◽  
Combination Rules ◽  
New Approach ◽  
Symptom Domains ◽  
Diagnosis Model

Since the information fusion based on Dempster-Shafer(D-S) evidence theory involves counter-intuitive behaviors when evidences highly conflict, a new approach of combination of weighted based on evidence closeness degree is proposed and applied in fault diagnosis．Firstly, the calculation method of evidence distance and evidential closeness degree as well as its revision are given according to the relevance and importance of various evidence sources; Then the closeness degree is normalized in order to obtain the weights of evidence sources. And the information fusion is realized with the base of weighted evidence theory. Finally, the approach is applied to fault diagnosis model of multi-evidences (multi-symptom domains or multi-sensors) in this paper. In the experiments, the proposed approach is compared with the existing combination rules and the results show that the reliability and accuracy of fault diagnosis are significantly improved and its uncertainty is decreased remarkably. The fusion problem given above is also solved effectively.

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