Adaptive Case Based Reasoning for Fault Diagnosis

A hybrid system of Case Based Reasoning (CBR) with Fuzzy ARTMAP (FAM) has been proposed to perform fault diagnosis for actuator system in DAMADICS benchmark. The hybrid system of CBR and FAM is for undertaking the stability plasticity dilemma for the incremental learning problem in CBR. At the same...

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Bibliographic Details
Main Authors: Pang, Shen Yee, Loo, Chu Kiong, Lim, Way Soong
Format: Conference or Workshop Item
Published: 2009
Subjects:
Online Access:http://shdl.mmu.edu.my/1931/
Description
Summary:A hybrid system of Case Based Reasoning (CBR) with Fuzzy ARTMAP (FAM) has been proposed to perform fault diagnosis for actuator system in DAMADICS benchmark. The hybrid system of CBR and FAM is for undertaking the stability plasticity dilemma for the incremental learning problem in CBR. At the same time, FAM can overcome the difficulty of indexing and retrieval in CBR as well as adaption of cases. FAM is used to make hypotheses and to guide the search of similar cases in the library, while CBR is used to select the most similar match for a given problem, supporting a particular hypothesis. A CBR system supports problem solving based on past experience with similar decision problems. The main strength lies in the fact that it enables directly reusing concrete examples in history and consequently eases the knowledge acquisition bottleneck.