Development of an equipment failure identification expert system with multiple reasoning approaches

Yeong Ho Ho, Huei Sen Wang, Hei-Chia Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The goal of the reasoning system in this study is to identify the most similar failure type or failure cases. As a user inputs all possible requirements (attributes), the inference engine of the system carries out its similarity assessment (inference approaches) and ranks rules or cases from the data base. Various inference approaches are chosen to find out the optimal method for the RBR and CBR system. The CBR system offers two types of inference methods which are hierarchical factors, flat factors without weight. For RBR system, there three types of inference methods are chosen, one is complete matched and the others are partial matched approaches which use the inference capability of CBR. The performance of developed system is then evaluated by using the real cases from China Steel Corporation (CSC). 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.

Original languageEnglish
Title of host publicationApplied Science and Precision Engineering Innovation
Pages1001-1005
Number of pages5
DOIs
Publication statusPublished - 2014 Jan 1
EventInternational Applied Science and Precision Engineering Conference 2013, ASPEC 2013 - NanTou, Taiwan
Duration: 2013 Oct 182013 Oct 22

Publication series

NameApplied Mechanics and Materials
Volume479-480
ISSN (Print)1660-9336
ISSN (Electronic)1662-7482

Other

OtherInternational Applied Science and Precision Engineering Conference 2013, ASPEC 2013
CountryTaiwan
CityNanTou
Period13-10-1813-10-22

Fingerprint

Inference engines
Expert systems
Steel
Industry

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Ho, Y. H., Wang, H. S., & Wang, H-C. (2014). Development of an equipment failure identification expert system with multiple reasoning approaches. In Applied Science and Precision Engineering Innovation (pp. 1001-1005). (Applied Mechanics and Materials; Vol. 479-480). https://doi.org/10.4028/www.scientific.net/AMM.479-480.1001
Ho, Yeong Ho ; Wang, Huei Sen ; Wang, Hei-Chia. / Development of an equipment failure identification expert system with multiple reasoning approaches. Applied Science and Precision Engineering Innovation. 2014. pp. 1001-1005 (Applied Mechanics and Materials).
@inproceedings{c2897a61c2f843cda6b429e579e2f9d0,
title = "Development of an equipment failure identification expert system with multiple reasoning approaches",
abstract = "The goal of the reasoning system in this study is to identify the most similar failure type or failure cases. As a user inputs all possible requirements (attributes), the inference engine of the system carries out its similarity assessment (inference approaches) and ranks rules or cases from the data base. Various inference approaches are chosen to find out the optimal method for the RBR and CBR system. The CBR system offers two types of inference methods which are hierarchical factors, flat factors without weight. For RBR system, there three types of inference methods are chosen, one is complete matched and the others are partial matched approaches which use the inference capability of CBR. The performance of developed system is then evaluated by using the real cases from China Steel Corporation (CSC). 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.",
author = "Ho, {Yeong Ho} and Wang, {Huei Sen} and Hei-Chia Wang",
year = "2014",
month = "1",
day = "1",
doi = "10.4028/www.scientific.net/AMM.479-480.1001",
language = "English",
isbn = "9783037859476",
series = "Applied Mechanics and Materials",
pages = "1001--1005",
booktitle = "Applied Science and Precision Engineering Innovation",

}

Ho, YH, Wang, HS & Wang, H-C 2014, Development of an equipment failure identification expert system with multiple reasoning approaches. in Applied Science and Precision Engineering Innovation. Applied Mechanics and Materials, vol. 479-480, pp. 1001-1005, International Applied Science and Precision Engineering Conference 2013, ASPEC 2013, NanTou, Taiwan, 13-10-18. https://doi.org/10.4028/www.scientific.net/AMM.479-480.1001

Development of an equipment failure identification expert system with multiple reasoning approaches. / Ho, Yeong Ho; Wang, Huei Sen; Wang, Hei-Chia.

Applied Science and Precision Engineering Innovation. 2014. p. 1001-1005 (Applied Mechanics and Materials; Vol. 479-480).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Development of an equipment failure identification expert system with multiple reasoning approaches

AU - Ho, Yeong Ho

AU - Wang, Huei Sen

AU - Wang, Hei-Chia

PY - 2014/1/1

Y1 - 2014/1/1

N2 - The goal of the reasoning system in this study is to identify the most similar failure type or failure cases. As a user inputs all possible requirements (attributes), the inference engine of the system carries out its similarity assessment (inference approaches) and ranks rules or cases from the data base. Various inference approaches are chosen to find out the optimal method for the RBR and CBR system. The CBR system offers two types of inference methods which are hierarchical factors, flat factors without weight. For RBR system, there three types of inference methods are chosen, one is complete matched and the others are partial matched approaches which use the inference capability of CBR. The performance of developed system is then evaluated by using the real cases from China Steel Corporation (CSC). 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 - The goal of the reasoning system in this study is to identify the most similar failure type or failure cases. As a user inputs all possible requirements (attributes), the inference engine of the system carries out its similarity assessment (inference approaches) and ranks rules or cases from the data base. Various inference approaches are chosen to find out the optimal method for the RBR and CBR system. The CBR system offers two types of inference methods which are hierarchical factors, flat factors without weight. For RBR system, there three types of inference methods are chosen, one is complete matched and the others are partial matched approaches which use the inference capability of CBR. The performance of developed system is then evaluated by using the real cases from China Steel Corporation (CSC). 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=84891129725&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84891129725&partnerID=8YFLogxK

U2 - 10.4028/www.scientific.net/AMM.479-480.1001

DO - 10.4028/www.scientific.net/AMM.479-480.1001

M3 - Conference contribution

SN - 9783037859476

T3 - Applied Mechanics and Materials

SP - 1001

EP - 1005

BT - Applied Science and Precision Engineering Innovation

ER -

Ho YH, Wang HS, Wang H-C. Development of an equipment failure identification expert system with multiple reasoning approaches. In Applied Science and Precision Engineering Innovation. 2014. p. 1001-1005. (Applied Mechanics and Materials). https://doi.org/10.4028/www.scientific.net/AMM.479-480.1001