Research on the Algorithm of Target Recognition Based on Two-leveled RBF Neural Network and D-S Evidence Theory

2006 ◽  
Author(s):  
Pan Xiuqin ◽  
Cao Yongcun ◽  
Lu Yong ◽  
Li Xiali ◽  
Zhao Yue
Keyword(s):  
Neural Network ◽  
Target Recognition ◽  
Rbf Neural Network ◽  
Evidence Theory

scholarly journals A Multimodel Decision Fusion Method Based on DCNN-IDST for Fault Diagnosis of Rolling Bearing

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Weixiao Xu ◽  
Luyang Jing ◽  
Jiwen Tan ◽  
Lianchen Dou
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rbf Neural Network ◽  
Evidence Theory ◽  
Rolling Bearing ◽  
Original Data ◽  
Decision Fusion ◽  
Fusion Method ◽  
Rolling Bearings ◽  
Advantages And Disadvantages

Each pattern recognition method has its advantages and disadvantages to diagnose the state of rotating machinery. There are many fault types of rolling bearings with apparent uncertainty. The optimal fusion level is usually challenging to be selected for a specific fault diagnosis task, and extensive human labour and prior knowledge are also highly required during these selections. To solve the above problems, a multimodel decision fusion method based on Deep Convolutional Neural Network and Improved Dempster-Shafer Evidence Theory (DCNN-IDST) is proposed for the inspection of rolling bearing. To solve the defect of the original evidence theory method in the fusion of high-conflict evidence, the fuzzy consistency matrix is introduced. By calculating the factor weight, the reliability and rationality of D-S evidence theory are improved. The DCNN model can learn features from the original data and carry out adaptive feature extraction for multiple sensor information. The features extracted by DCNN adaptively are input into multiple network models for decision fusion. The new method of DCNN-IDST multimodel decision fusion is applied to detect the damage of rolling bearings. To evaluate the effectiveness of the proposed method, both the BP neural network and RBF neural network are used to set up a multigroup comparison test. The result demonstrates that the proposed method can detect the fault of the rolling bearing effectively and achieve the highest diagnosis accuracy among all the tested methods in the experiment.

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.

scholarly journals Automatic Transmission Fault Symptom Identification by Apply of Neural Network and D-S Evidence Theory

2021 ◽  
pp. 778-799
Author(s):  
RyongSik O ◽  
Jiangwei Chu ◽  
Zhenwei Sun ◽  
Myongchol Ri ◽  
MyongSu Sim ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Fuzzy Neural Network ◽  
Rbf Neural Network ◽  
Automatic Transmission ◽  
Evidence Theory ◽  
Test Results ◽  
The Neural Network ◽  
Initial Symptoms ◽  
Fuzzy Neural

At present, the method of identifying the fault symptoms of various machines by combining the neural network and the D-S evidence theory is attracting attention from researchers because the identification time is fast and the diagnosis is accurate. In this paper, it was mentioned a method for identifying the fault symptoms of automatic transmission by combining these two theories. First, it was mentioned a method for identifying fault symptoms of the automatic transmission by combining a fuzzy neural network and an RBF neural network. Next, it was newly described a method to improve the accuracy of fault symptom identification by the D-S evidence theory. In addition, the accuracy of this method was verified by an experimental method. In the experiment Firstly, two sub neural networks are established to recognize the initial symptoms. That is, the first sub-neural network E1 be used as the fuzzy neural network, the second sub-neural network E2 be used as RBF neural network, respectively, for preliminary symptom recognition. And then, these outputs of the two sub neural networks are used as the evidence space of D-S evidence theory, so the global diagnosis is carried out. The results show that the test results are consistent with the actual fault symptoms. The success rate of fault diagnosis up to 96.3%, therefore, on the identification of the automatic transmission fault symptom, effectiveness, and feasibility of the D-S evidence theory based on information fusion is verified.

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