Knowledge Integration Strategies in Defect Diagnosis

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Title: Knowledge Integration Strategies in Defect Diagnosis
Author: Hsieh, Sheng-Jen
Abstract: Defect diagnosis—defined here as the process of evaluating and locating the true cause of a defect type—has been an island of automation and a time consuming and non -productive task . Needed are efficient and cost -effective methods which facilitate the task . The purpose of this research is to develop hybrid mathematical /simulation models and algorithms to diagnose defects with (I ) multiple causes , (2 ) unknown cause probability , and (3 ) uncertain knowledge , with the objective of minimizing cost and number of trials . The research problem is tackled in three phases . First , a diagnostic tree structure is proposed to (I ) categorize diagnostic knowledge into sets of cause -effect relationships ; and (2 ) simultaneously incorporate both testing costs and production loss . Then propositions for knowledge integration are developed to integrate initial and current knowledge , which correspond to the strength values for each edge within the diagnostic tree . Through the integration process , initial uncertain knowledge will be gradually pruned with newly arriving certain knowledge as the diagnosis task continues . Finally , primary elements of the conceptual decision process for troubleshooting defects are represented in a flow chart . Based on these ingredients , a linear multi -stage mathematic model is formulated , and a variety of knowledge integration strategies proposed . Second , the problem of searching for the cause of a defect is formulated as a search problem where the estimated cause variable resembles a sensor function , and the true cause variable represents the target function . Therefore , the problem becomes a mapping of one function to the other . Several learning algorithms are created based on these developments . Then the algorithms are transformed into a probabilistic learning model where Monte -Carlo simulation is utilized to assess the performance of each algorithm . Primary propositions , lemmas and analytic properties are developed in this phase . Third , a variety of experiments are used to investigate and compare the algorithms' (I ) learning and fault -tolerant properties , (2 ) cost and trials performance , and (3 ) computational efficiency . Experimental results indicate that the proposed methods are superior to general techniques such as sequential and random searches in minimizing number of trials and costs . In addition , the proposed methods also contain learning and fault -tolerant properties .
URI: http : / /hdl .handle .net /2346 /21901
Date: 1995-08


Knowledge Integration Strategies in Defect Diagnosis. Doctoral dissertation, Texas Tech University. Available electronically from http : / /hdl .handle .net /2346 /21901 .

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