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A hybrid failure diagnosis and prediction using natural language-based process map and rule-based expert system

Citation

Kim, D and Lin, Y and Lee, S and Kang, BH and Han, SC, A hybrid failure diagnosis and prediction using natural language-based process map and rule-based expert system, International Journal of Computer, Communications and Control, 13, (2) pp. 175-191. ISSN 1841-9836 (2018) [Refereed Article]


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Copyright Statement

Copyright (c) 2018 Dohyeong Kim, Yingru Lin, Sungyoung Lee, Byeong Ho Kang, Soyeon Caren Han. Licensed under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) https://creativecommons.org/licenses/by-nc/4.0/

DOI: doi:10.15837/ijccc.2018.2.3189

Abstract

Preventive maintenance is required in large scale industries to facilitate highly efficient performance. The efficiency of production can be maximized by preventing the failure of facilities in advance. Typically, regular maintenance is conducted manually in which case, it is hard to prevent repeated failures. Also, since measures to prevent failure depend on proactive problem-solving by the facility expert, they have limitations when the expert is absent, or any error in diagnosis is made by an unskilled expert. In many cases, an alarm system is used to aid manual facility diagnosis and early detection. However, it is not efficient in practice, since it is designed to simply collect information and is activated even with small problems. In this paper, we designed and developed an automated preventive maintenance system using expertsí experience in detecting failure, determining the cause, and predicting future system failure. There are two main functions in order to acquire and analyze domain expertise. First, we proposed the network-based process map that can extract the expertís knowledge of the written failure report. Secondly, we designed and implemented an incremental learning rule-based expert system with alarm data and failure case. The evaluation results shows that the combination of two main functions works better than another failure diagnosis and prediction frameworks.

Item Details

Item Type:Refereed Article
Keywords:expert's knowledge, preventive maintenance, failure prediction, alarm management, knowledge reuse
Research Division:Information and Computing Sciences
Research Group:Library and Information Studies
Research Field:Information Retrieval and Web Search
Objective Division:Information and Communication Services
Objective Group:Computer Software and Services
Objective Field:Information Processing Services (incl. Data Entry and Capture)
UTAS Author:Lin, Y ( Yingru Lin)
UTAS Author:Kang, BH (Professor Byeong Kang)
ID Code:125755
Year Published:2018 (online first 2017)
Web of Science® Times Cited:1
Deposited By:Information and Communication Technology
Deposited On:2018-05-03
Last Modified:2018-09-06
Downloads:32 View Download Statistics

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