TY - GEN
T1 - Developing an expert system of failure analysis
AU - Ho, Yeong Ho
AU - Wang, Huei Sen
AU - Wang, Hei Chia
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - An Equipment Failure Analysis Expert System (EFAES) is to be developed to help the engineers diagnose the causes of the failure mechanism and provide a reliable remedy. This system is based on an innovative reasoning approach: integrating the rule-based reasoning (RBR) and the case-based reasoning (CBR) methods The architecture developed in the system consists of six major elements-"Factor and Attribute Editor", Knowledge Actuation Interface", "Knowledge Base", "User Interface", "Inference Engine" and "Explanation Facility". Here, the RBR system consists of 46 failure mechanisms and their rules. The CBR system consists of 586 failure cases which are coded and composed from 23 factors and their 265 attributes. Also, this system provides a variety of inference methods which allows retrieving the best answers to users. For the RBR system, performance is directly check the inferred order of the document ranking list. For the CBR system, the effectiveness of each inference method is evaluated by using "Recall", "Precision", and "F-Measure" approaches. From the test results, many recommendations are proposed.
AB - An Equipment Failure Analysis Expert System (EFAES) is to be developed to help the engineers diagnose the causes of the failure mechanism and provide a reliable remedy. This system is based on an innovative reasoning approach: integrating the rule-based reasoning (RBR) and the case-based reasoning (CBR) methods The architecture developed in the system consists of six major elements-"Factor and Attribute Editor", Knowledge Actuation Interface", "Knowledge Base", "User Interface", "Inference Engine" and "Explanation Facility". Here, the RBR system consists of 46 failure mechanisms and their rules. The CBR system consists of 586 failure cases which are coded and composed from 23 factors and their 265 attributes. Also, this system provides a variety of inference methods which allows retrieving the best answers to users. For the RBR system, performance is directly check the inferred order of the document ranking list. For the CBR system, the effectiveness of each inference method is evaluated by using "Recall", "Precision", and "F-Measure" approaches. From the test results, many recommendations are proposed.
UR - http://www.scopus.com/inward/record.url?scp=84873927616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873927616&partnerID=8YFLogxK
U2 - 10.4028/www.scientific.net/AMM.284-287.2375
DO - 10.4028/www.scientific.net/AMM.284-287.2375
M3 - Conference contribution
AN - SCOPUS:84873927616
SN - 9783037856123
T3 - Applied Mechanics and Materials
SP - 2375
EP - 2379
BT - Innovation for Applied Science and Technology
T2 - 2nd International Conference on Engineering and Technology Innovation 2012, ICETI 2012
Y2 - 2 November 2012 through 6 November 2012
ER -