(67) Experimental Adaptive Model-based Diagnostic System

	Machine:     Multi-PSI
	Environment: PIMOS
	Language:    KL1
	Source Code: 2.4 MB
	Documents:   None

Overview

An adaptive diagnostic system with learning capability.

Characteristics

Model-based diagnosis
It performs model-based diagnosis, utilizing knowledge about the structure of the target device and the function of each component. This eliminates the necessity of interviewing experts in order to build a diagnostic knowledge base.

Adaptive diagnosis mechanism with learning capability
It learns fault probability distribution, based on its diagnostic experience, in order to pinpoint the faulty component with minimum number of tests.

Parallel processing
Parallel computation on Multi-PSI machine reduces the computation time for the diagnosis and the learning function.

Function

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

Then the system calculates the suspect list for given symptom and observation data. This task is performed by utilizing a model-based knowledge which models behavior and interconnection of the target device components. This model knowledge is represented in first-order predicate scheme and written in the logic programming language KL1. The system computes the suspect list by a hypothetical reasoning which calculates a set of explanations of giving the faulty output for the given input data on the device. This function is also complex and implemented in parallel.

FTP

www-admin@icot.or.jp