Fault Diagnosis of Shearer Based on Fuzzy Inference

2011 ◽  
Vol 52-54 ◽  
pp. 1577-1580 ◽  
Author(s):  
Feng Gao ◽  
Lin Jing Xiao ◽  
Wei Yan Zhong ◽  
Wei Liu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Inference ◽  
Characteristic Parameter ◽  
Fuzzy Relation ◽  
Inference System ◽  
Knowledge Expression ◽  
Human Thinking ◽  
Fuzzy Relation Matrix ◽  
Fuzzy Vector ◽  
Relation Matrix

The purpose of this study is to provide a correct and timely diagnosis mechanism of shearer failures by knowledge acquisition through a fuzzy inference system which could approximate expert experience. Concerning a question of uncertain knowledge expression and reasoning in shearer malfunction, the fuzzy inference theory is used in shearer malfunction fault diagnosis. The fuzzy relation matrix of faults and signs is deduced based on deep research of failure mechanism and expert experience, which agrees with fault and fault symptoms non one-to-one relationship and human thinking. Fault characteristic parameter is calculated to corresponding subordinate degree, then is operated with fuzzy relation matrix and get fault fuzzy vector. Finally, the shearer malfunction fault is diagnosed according to certain diagnosis principle. The example proves that the method has less calculation, explicit conclusion and other merits.

Fault Diagnosis of Construction Elevator Based on Fuzzy Recognition

2012 ◽  
Vol 529 ◽  
pp. 459-462
Author(s):  
Li Mei Liu ◽  
Jian Bin Li
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Relation ◽  
Recognition Method ◽  
Diagnosis System ◽  
Fault Diagnosis System ◽  
Fuzzy Relation Matrix ◽  
Fuzzy Recognition ◽  
Fuzzy Vector ◽  
Relation Matrix

Fault eigenvector, fault fuzzy vector and fuzzy relation matrix of the construction elevator is built on using the fuzzy recognition method in the fault diagnosis system of the elevator. On the basis of every fault membership obtained through fuzzy recognition, the equipment fault is confirmed according to max membership principle. The test shows it can effectively help to estimate cause of fault and to avoid the accidents when the fuzzy recognition method is used.

scholarly journals Fault Diagnosis Based on Fuzzy Relation Matrix and Recognition

1999 ◽  
Vol 32 (2) ◽  
pp. 7844-7848
Author(s):  
Ying Huo ◽  
Chaozhen Hou ◽  
Shuangxi Liu
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Relation ◽  
Fuzzy Relation Matrix ◽  
Relation Matrix

Measures of uncertainty for a fuzzy probabilistic approximation space

2021 ◽  
pp. 1-24
Author(s):  
Lijun Chen ◽  
Damei Luo ◽  
Pei Wang ◽  
Zhaowen Li ◽  
Ningxin Xie
Keyword(s):  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Fuzzy Relation ◽  
Point Of View ◽  
Approximation Space ◽  
Fuzzy Approximation ◽  
Entropy Measurement ◽  
Fuzzy Relation Matrix ◽  
Relation Matrix

 An approximation space (A-space) is the base of rough set theory and a fuzzy approximation space (FA-space) can be seen as an A-space under the fuzzy environment. A fuzzy probability approximation space (FPA-space) is obtained by putting probability distribution into an FA-space. In this way, it combines three types of uncertainty (i.e., fuzziness, probability and roughness). This article is devoted to measuring the uncertainty for an FPA-space. A fuzzy relation matrix is first proposed by introducing the probability into a given fuzzy relation matrix, and on this basis, it is expanded to an FA-space. Then, granularity measurement for an FPA-space is investigated. Next, information entropy measurement and rough entropy measurement for an FPA-space are proposed. Moreover, information amount in an FPA-space is considered. Finally, a numerical example is given to verify the feasibility of the proposed measures, and the effectiveness analysis is carried out from the point of view of statistics. Since three types of important theories (i.e., fuzzy set theory, probability theory and rough set theory) are clustered in an FPA-space, the obtained results may be useful for dealing with practice problems with a sort of uncertainty.

Fault Diagnosis of Roller Bearing Conditions Using ANFIS

2009 ◽  
Vol 16-19 ◽  
pp. 886-890 ◽  
Author(s):  
Wen Tao Sui ◽  
Dan Zhang
Keyword(s):  
Feature Selection ◽  
Fault Diagnosis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Roller Bearing ◽  
Back Propagation ◽  
Selection Criterion ◽  
Data Set ◽  
Inference System ◽  
Roller Bearings

This paper presents a fault diagnosis method on roller bearings based on adaptive neuro-fuzzy inference system (ANFIS) in combination with feature selection. The class separability index was used as a feature selection criterion to select pertinent features from data set. An adaptive neural-fuzzy inference system was trained and used as a diagnostic classifier. For comparison purposes, the back propagation neural networks (BPN) method was also investigated. The results indicate that the ANFIS model has potential for fault diagnosis of roller bearings.

scholarly journals Fault diagnosis of PV array using adaptive network based fuzzy inference system

2020 ◽  
Vol 467 ◽  
pp. 012083
Author(s):  
Yutao Gan ◽  
Zhicong Chen ◽  
Lijun Wu ◽  
Shuying Cheng ◽  
Peijie Lin
Keyword(s):  
Fault Diagnosis ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Adaptive Network ◽  
Inference System ◽  
Pv Array

scholarly journals Modelling Unconfined Groundwater Recharge Using Adaptive Neuro-Fuzzy Inference System

Processes ◽  
2020 ◽  
Vol 8 (10) ◽  
pp. 1280
Author(s):  
Khaled Mohamed Nabil I. Elsayed ◽  
Rabee Rustum ◽  
Adebayo J. Adeloye
Keyword(s):  
Groundwater Recharge ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Monitoring Site ◽  
Data Set ◽  
Inference System ◽  
The North ◽  
Neuro Fuzzy ◽  
Ground Water Recharge ◽  
Human Thinking

Estimating groundwater recharge using mathematical models such as water budget or soil water balance method has been proved to be very difficult due to the complex, uncertain multidimensional nature of the process, despite the simplicity of the concept. Artificial Intelligence (AI) techniques have been proposed to deal with this complexity and uncertainty in a similar way to human thinking and reasoning. This study proposed the use of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to model unconfined groundwater recharge using a set of data records from Kaharoa monitoring site in the North Island of New Zealand. Fifty-three data points, comprising a set of input parameters such as rainfall, temperature, sunshine hours, and radiation, for a period of approximately four and a half years, have been used to estimate ground water recharge. The results suggest that the ANFIS model is overall a reliable estimator for groundwater recharge, the correlation coefficient of the model reached 93% using independent data set. The method is easy, flexible and reliable; hence, it is recommended to be used for similar applications.

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