A hybrid approach of rough set theory and genetic algorithm for fault diagnosis

2005 ◽  
Vol 27 (1-2) ◽  
pp. 119-127 ◽  
Author(s):  
C.L. Huang ◽  
T.S. Li ◽  
T.K. Peng
Keyword(s):  
Genetic Algorithm ◽  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Hybrid Approach

Research on Fault Diagnosis of Forklift’s Hydraulic System Based on Rough Set Theory

2012 ◽  
Vol 524-527 ◽  
pp. 819-823
Author(s):  
Xin Ping Su ◽  
Guang Kun Nie ◽  
Wei Xin Fan
Keyword(s):  
Fault Tolerance ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Set Theory ◽  
Rough Set ◽  
Hydraulic System ◽  
Rough Set Theory ◽  
Attribute Reduction ◽  
Train Of Thought ◽  
Fault Information

An approach of forklift’s fault diagnostic knowledge acquisition and discrete date based on rough set theory was put forward, according to the rough set theory in fault diagnosis of fault tolerance, the use of rough set theory in fault knowledge attribute reduction and value reduction, as in incomplete fault information of forklift hydraulic system fault diagnosis provides a train of thought. The inferential strategy and process of fault diagnosis of hydraulic system for forklift were described. Examples show that the proposed approach is very effective.

A rough set approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis

2001 ◽  
Vol 15 (3) ◽  
pp. 211-221 ◽  
Author(s):  
LI-PHENG KHOO ◽  
LIAN-YIN ZHAI
Keyword(s):  
Fault Diagnosis ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Operating Conditions ◽  
Prototype System ◽  
Novel Approach ◽  
Discrete Manner ◽  
Critical Machines ◽  
Concept Classification

The efficient use of critical machines or equipment in a manufacturing system requires reliable information about their current operating conditions. This information is often used as a basis for machine condition monitoring and fault diagnosis—which essentially is an endeavor of knowledge extraction. Rough set theory provides a novel way to knowledge acquisition, especially when dealing with vagueness and uncertainty. It focuses on the discovery of patterns in incomplete and/or inconsistent data. However, rough set theory requires the data analyzed to be in discrete manner. This paper proposes a novel approach to the treatment of continuous-valued attributes in multi-concept classification for mechanical diagnosis using rough set theory. Based on the proposed approach, a prototype system called RClass-Plus has been developed. RClass-Plus is validated using a case study on mechanical fault diagnosis. Details of the validation are described.

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