A hybrid expert system for equipment failure analysis

Authors:
Hei-Chia Wang;Huei-Sen Wang
Affiliations:
Institute of Information Management, National Cheng Kung University, 1st University Road, Tainan 701, Taiwan;Department of Industrial Engineering and Management, Diwan Collage of Management, Madou, Tainan 721, Taiwan
Venue:
Expert Systems with Applications: An International Journal
Year:
2005

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Abstract

This paper outlines the development of a web-based expert system, equipment failure analysis expert system (EFAES), for the largest steel company in Taiwan. The EFAES inference engine employs both case-based reasoning (CBR) and rule-based reasoning (RBR) to generate a hybrid recommendation list for cross validation. Moreover, this inference engine was designed to support a hierarchical multi-attribute structure. Unlike the traditional 'flat' attribute structure, this hierarchical multi-attribute structure allows experts to weigh the attributes dynamically. Two two-dimensional matrixes, multi-attribute analysis (MAA) and subattributes matrix (SAM), are used to store the attributes' weight values. Normalized relative spending (NRS) is adapted to normalize the weight values for the inference engine. The system recommends both cases and rules, which can give more information in recognizing the failure types. According to our experimental results, applying our proposed method in an inference engine to analyze failure can result in better quality recommendations.