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.

The Motor Fault Diagnosis Based on Neural Network and the Theory of D-S Evidence

2013 ◽  
Vol 683 ◽  
pp. 881-884 ◽  
Author(s):  
Chang Fei Sun ◽  
Zhi Shan Duan ◽  
Yang Yang ◽  
Miao Wang ◽  
Li Jie Hu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Threshold Value ◽  
Fusion Technology ◽  
Independent Evidence ◽  
Modes Of Failure ◽  
Processing Data ◽  
Single Feature ◽  
Diagnosis Method

In order to reduce the uncertainty of the traditional method that use a single parameter in the motor fault diagnosis, create a reliable motor fault diagnosis model by using multivariate information fusion technology and the combination of neural network and the theory of D-S evidence .First, fusion the information of many kinds of sensors, preliminary identify the modes of failure, find the information of different fault feature by analysing and processing data, establish the domain of feature. Then part diagnose the domain of feature by using neural network. The local diagnosis results form independent evidence body. Calculate the credibility of the fault distribution of each evidence body for recognition framework. It is difficult to discriminant fault types by directly using these reliability distribution. So choose appropriate D-S evidence formula to fusion each evidence body and further process and analyse the information of evidence. The credibility of the distribution is nearer and nearer to the judgement threshold value of fault types with one fusion, and the rest of the credibility distribution of the fault is more and more smaller. So the basic reliability distribution has better peak and separability, the diagnose results is more accurate, and finally achieve accurate diagnosis of the motor fault. The diagnosis example shows that the diagnosis method based on neural network and the theory of D-S evidence can realize comprehensive diagnosis of motor fault by using multi-resources information. The reliability and accuracy of this diagnosis method are far higher than that of the local diagnosis using single feature. It improves the precision of the motor 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.

Application of Integrated Neural Network Information Fusion in Fault Diagnosis of one Missile Launching Control Unit

2013 ◽  
Vol 427-429 ◽  
pp. 2808-2812
Author(s):  
Xu De Cheng ◽  
Hong Li Wang ◽  
Bing Xu ◽  
Xue Dong Xue
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Information Fusion ◽  
Preliminary Analysis ◽  
Control Unit ◽  
Fusion Technology ◽  
Network Information ◽  
Diagnosis Rate ◽  
Fault Diagnosis System ◽  
Made In

Research and development of fault diagnosis system in application of integrated neural network information fusion is based on information fusion technology, with which preliminary analysis of equipment fault is made in different perspectives in terms of neural network, so as to identify the fault on the basis of fusion outcome. This technique is applied in fault diagnosis of one type of missile launching control unit, leading to sufficient use of various information and substantially increased fault diagnosis rate.

scholarly journals Application Research of Multi-source Information Fusion Technology in Power Network Fault Diagnosis

2019 ◽  
Vol 1187 (2) ◽  
pp. 022034
Author(s):  
Shuxin Liu ◽  
Enmin Zhao ◽  
Yanjun Zhang ◽  
Jing Li ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Power Network ◽  
Fusion Technology ◽  
Application Research ◽  
Source Information ◽  
Network Fault

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.

scholarly journals Application of Multi-sensor Information Fusion Technology on Fault Diagnosis of Electrical System

2017 ◽  
Vol 11 ◽  
pp. 05003
Author(s):  
Ling-Wen Meng ◽  
Ji-Pu Gao ◽  
Ming-Yong Xin ◽  
Jin-Mei Xiong ◽  
Guo Rui
Keyword(s):  
Fault Diagnosis ◽  
Information Fusion ◽  
Fusion Technology ◽  
Electrical System ◽  
Sensor Information
Sign in / Sign up

Export Citation Format

Share Document