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.

Application of Neural Networks in Fault Diagnosis of Rotating Machinery

2007 ◽  
Author(s):  
Qing He ◽  
Dongmei Du
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fault Diagnosis ◽  
Large Scale ◽  
Object Oriented ◽  
Rotating Machinery ◽  
Turbine Generator ◽  
Symptom Domains ◽  
Diagnosis Method ◽  
Artificial Neural

The fault diagnosis method based on artificial neural networks is summarized. An object-oriented paradigm is introduced to fault diagnosis for large scale rotating machinery, for example, turbine-generator. A fault diagnosis method based on object-oriented artificial neural networks for more symptom domains is presented. The training patterns are constructed. A treatment for incomplete symptom domains and/or concurrent faults in diagnosing is given. Verification is carried out for the actual turbine-generator data with incomplete symptom domains.

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.

Data-Driven Based Gas Path Fault Diagnosis for Turbo-Shaft Engine

2012 ◽  
Vol 249-250 ◽  
pp. 400-404 ◽  
Author(s):  
Feng Lu ◽  
Tie Bin Zhu ◽  
Yi Qiu Lv
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Rbf Neural Network ◽  
Evidence Theory ◽  
Aircraft Engine ◽  
Data Driven ◽  
Support Vector ◽  
Diagnosis Method ◽  
Gas Path Fault

In order to improve diagnostic accuracy and reduce the rate of misdiagnosis to the aircraft engine gas path faulty, the methods based on data-driven and information fusion are developed and analyzed. BP neural network (NN) and RBF neural network based on data-driven single gas path fault diagnosis method is introduced firstly. Design gas path performance estimators and the fault type classification for turbo-shaft engine. Then the gas path fused diagnostic structure based on D-S evidence theory and least squares support vector machine are developed. Comparisons of the turbo-shaft engine gas path fault diagnosis verify the feasibility and effectiveness of the gas path fault diagnosis based on information fusion.

Simultaneous Fault Diagnosis of the Reactor Coolant System Based on the DSM Evidence Theory

2013 ◽  
Author(s):  
Jinshen Ren ◽  
Botao Jiang ◽  
Fuyu Zhao
Keyword(s):  
Fault Diagnosis ◽  
Evidence Theory ◽  
Uncertain Information ◽  
Information Uncertainty ◽  
Test Bed ◽  
Reactor Coolant ◽  
Wide Range ◽  
Diagnosis Method ◽  
Sensor Information ◽  
Coolant System

Fault diagnosis of nuclear power plant (NPP) has become a major research topic in ensuring the reliability and safety for the NPP operation. In the light of complexity and information uncertainty of nuclear reactor coolant system, some diagnosis techniques based on the multi-sensor information fusion theories have been widely used. As one part of the those theories, the Dempster-Shafer (D-S) theory excelled in demonstrating and combining uncertain information has witnessed a wide range of applications in the fault diagnosis fields. It can be inferred from the previous studies that the fault diagnosis method based on the D-S theory is efficient; However, this method is mainly intended for single fault diagnosis. In fact, in practical application, simultaneous faults often occur. Based on the DSm theory (Dezert-Smarandache theory of plausible and paradoxical reasoning), this paper proposes a simultaneous fault diagnosis method, which is intended to deal with the simultaneous faults in the reactor coolant system and adequately cope with the sensor information uncertainty. This method can be divided into four steps. Firstly, with the features of the simultaneous fault considered, a diagnosis model of the simultaneous fault is designed, which is based on the DSm theory. Secondly, for the instant information of each senor, the fuzzy membership degree is used to set the corresponding basic probability assignment (BPA) function for each sensor. Thirdly, the above-mentioned BPA functions are combined by utilizing the classic DSm rules. Finally, a decision on the basis of maximum support rule and absolute support rule is made to determine the real fault modes. After that, the experiments on the test-bed are conducted to prove the efficiency of this method.

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.

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