RESEARCH GOAL 

Advances in modern technology have produced complex electronic devices, 
such as switching systems, and their maintenance tasks have become seri-
ously difficult. A solution to automate the maintenance task is the expert 
system technology, which represents interviewed expert knowledge in a rule 
form. However, the technology has been applied to only small scale devices, 
because the knowledge base construction is difficult. To eliminate this prob-
lem, model-based diagnosis approaches have been investigated. Since model 
based systems utilize design knowledge of target device structure and be- 
havior, they do not require expert interviews. However, because they lack 
experiential knowledge of human experts, it can not perform efficiently. 

The research goals are, to add a function to utilize experiential knowledge to 
model-based diagnosis systems, to realize an efficient learning function which 
acquires the experiential knowledge from past cases, and to realize parallel 
implementations of the complex functions for fast computation. 

SYSTEM OVERVIEW 




Basic System Flow 

The learning function of the system first estimates fault probability dis-
tribution for each component from the past case record. This learning is 
performed by an inductive learning algorithm, which is based on MDL mea-
surement. This complex task is processed in parallel. 


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