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.

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.

The Application of Improved SVM in Mixed Circuit

2012 ◽  
Vol 224 ◽  
pp. 493-496 ◽  
Author(s):  
Huai Long Wang ◽  
Qiang Pan ◽  
Hong Liu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Small Samples ◽  
Lifting Wavelet Transform ◽  
Fault Features ◽  
Diagnosis Method ◽  
Lifting Wavelet ◽  
Current Characteristics ◽  
Mixed Circuit ◽  
Better Than

In order to improve the speed and the rate of fault diagnosis in mixed circuit, this paper introduces a new fault diagnosis method. Through extracting fault features of current characteristics effectively and applying to Improved SVM, the ability of pattern recognition will be better than the traditional BP Neural Network and Single SVM, especially in small samples or non-linear cases. Meanwhile, this paper presents the lifting wavelet transform in order to obtain the feature information accurately. The accuracy of fault diagnosis can greatly enhance by discussing the Improved SVM combined with lifting wavelet transform in a specific monostable trigger. That points out a new direction for the fault diagnosis of mixed circuit.

Fault Diagnosis System Based on Fuzzy Theory of Vehicle Air Conditioner

2013 ◽  
Vol 329 ◽  
pp. 324-328
Author(s):  
Ying Hui Wang ◽  
Shu Sheng Xiong ◽  
Wen Lang ◽  
Yi Tian Tang
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Air Conditioner ◽  
Diagnosis System ◽  
Diagnosis Method ◽  
Fault Diagnosis System

To maintain the vehicle air conditioner efficiently, a fault diagnosis system based on fuzzy theory is prospected in this paper. A fault diagnosis method based on fuzzy theory was given. And according to the method, a fault diagnosis program was written with labview. Experiments proved that the fault diagnosis program was stable and functional. The accuracy of this fault diagnosis system is more than 80%. The system can be used to diagnosed the malfunction of vehicle air conditioner efficiently and discover the potential fault in time, helping to eliminate hidden dangers.

Research on Fault Diagnosis Strategy of Belt Conveyor

2013 ◽  
Vol 312 ◽  
pp. 607-610 ◽  
Author(s):  
Wei Hu ◽  
Ou Li
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Information Fusion ◽  
Accurate Diagnosis ◽  
Experimental Result ◽  
Fusion Center ◽  
Fusion Technology ◽  
Fuzzy Information ◽  
Belt Conveyor ◽  
Diagnosis Method

In view of the inadequacy of the fault diagnosis of the belt conveyor, the paper takes advantage of the application of fuzzy information fusion technology to fault diagnosis, based on the fuzzy set theory, a fault diagnosis method based on Multi-sensor fuzzy information fusion is developed. The obtain information of many sensors will fuzzy, again its fusion based on the synthetic operation and decision-making rules of the fusion center, in order to gain the accurate state estimation and judgment of belt conveyor. The experimental result indicates that the credibility of diagnosis is improved markedly and the uncertainty is reduced significantly after the multi-sensor fuzzy information fusion, the accurate diagnosis to belt conveyor is realized.

scholarly journals An SDP Characteristic Information Fusion-Based CNN Vibration Fault Diagnosis Method

2019 ◽  
Vol 2019 ◽  
pp. 1-14 ◽  
Author(s):  
Xiaoxun Zhu ◽  
Jianhong Zhao ◽  
Dongnan Hou ◽  
Zhonghe Han
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Fault Identification ◽  
High Complexity ◽  
Test Rig ◽  
Rotor Vibration ◽  
Vibration Signals ◽  
Image Identification ◽  
Diagnosis Method

This study proposes a symmetrized dot pattern (SDP) characteristic information fusion-based convolutional neural network (CNN) fault diagnosis method to resolve issues of high complexity, nonlinearity, and instability in original rotor vibration signals. The method was used to conduct information fusion of real modal components of vibration signals and SDP image identification using CNN in order to achieve vibration fault diagnosis. Compared with other graphic processing methods, the proposed method more fully expressed the characteristics of different vibration signals and thus presented variations between different vibration states in a simpler and more intuitive way. The proposed method was experimentally investigated using simulation signals and rotor test-rig signals, and its validity and advancements were demonstrated using experimental analysis. By using CNN through deep learning to adaptively extract SDP characteristic information, vibration fault identification was ultimately realized.

Full Information Fusion and its Application in Fault Diagnosis for Rotary Machinery

2011 ◽  
Vol 66-68 ◽  
pp. 1315-1319 ◽  
Author(s):  
Xin Min Dong ◽  
Jie Han ◽  
Wang Shen Hao
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Engineering Practice ◽  
Full Information ◽  
Single Section ◽  
Rotary Machinery ◽  
Diagnosis Method ◽  
Diagnosis Accuracy

The rotor motion and the information fusion of single section were discussed; the fault diagnosis method for rotary machinery based on the full information fusion of two sections was put forward, and the back propagation neural network model was established. Engineering practice indicated that the fault diagnosis accuracy based on the information fusion of two sections was higher than that based on the information fusion of single section.

Fuzzy Diagnosis of Tapping Process Fault

2009 ◽  
Vol 626-627 ◽  
pp. 207-212 ◽  
Author(s):  
H.L. Xue ◽  
Le Wang ◽  
Xue Yong Chen ◽  
Gui Cheng Wang
Keyword(s):  
Experimental Study ◽  
Fault Diagnosis ◽  
Fuzzy Theory ◽  
Weight Matrix ◽  
Diagnosis System ◽  
Process Failure ◽  
Diagnosis Method ◽  
Fault Diagnosis System ◽  
Fuzzy Diagnosis ◽  
Types Of Faults

Based on analyzing the problem of tap worn and broken in the tapping process, the faults in tapping process are classified into four types: tap worn, chips jamming, uneven hardness of material and the tapping process failure; According to the fuzzy theory, this paper describes the torque characteristic of the four types of faults, ascertains the characteristic vector of fault, presents the weight matrix among faults, puts forward the judgment method of system faults and establishes the fuzzy fault diagnosis system in tapping process. The experimental study shows that the fuzzy diagnosis method can effectively identify the four types of faults in tapping process and guard against tap broken.

Sign in / Sign up

Export Citation Format

Share Document