Abstract
The paper presents an object oriented fault diagnostic expert system framework which analyses observations from the unit under test when fault occurs and infers the causes of failures. The frame work is characterized by two basic features. The first includes a fault diagnostic strategy which utilizes the fault classification and checks knowledge about unit under test. The fault classification knowledge reduces the complexity in fault diagnosis by partitioning the fault section. The second characteristic is object oriented inference mechanism using backward chaining with message passing within objects. The refractoriness and recency property of inference mechanism improve efficiency in fault diagnosis. The developed framework demonstrates its effectiveness and superiority compared to earlier approaches
Highlights
Expert Systems have traditionally been built using large collection of rules based on empirical associations; Interest has built up recently in use of Artificial Intelligence techniques that reason from first principles i.e. from an understanding of causality of the device being diagnosed
Expert system that reason based on understanding of the structure and function of the unit under test has been explored in number of domains, including medicine [2,3], computer fault diagnosis [4], automobile engine fault diagnosis [5], and electronics equipment fault diagnosis [6]
Fault diagnosis methodology operates on observed erroneous behavior and hardware structure of the unit under test
Summary
& Engineering, DKTE Textile & Engineering Institute Ichalkaranji, INDIA. Professor in Electronics Engineering, Rajshree Shau College of Engineering, Pune.INDIA. Abstract -The paper presents an object oriented fault diagnostic expert system framework which analyses observations from the unit under test when fault occurs and infers the causes of failures. The frame work is characterized by two basic features. The first includes a fault diagnostic strategy which utilizes the fault classification and checks knowledge about unit under test. The fault classification knowledge reduces the complexity in fault diagnosis by partitioning the fault section. The second characteristic is object oriented inference mechanism using backward chaining with message passing within objects. The refractoriness and recency property of inference mechanism improve efficiency in fault diagnosis. The developed framework demonstrates its effectiveness and superiority compared to earlier approaches
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