INSIDE: A neuronet based hardware fault diagnostic system
Abstract
An inertial navigation system interactive diagnostic expert (INSIDE) was developed for troubleshooting an avionic line-replaceable unit, the inertial navigation system. INSIDE was designed based on a neural network model called neural-logic network. The knowledge base can be constructed using a neural-logic network by learning from past cases recorded in the workshop log book. To complement the connectionist knowledge base, a flowchart module which captures the knowledge of troubleshooting flowcharts was also implemented as part of the system. During operation, if the connectionist module fails to derive the solution, the user will be directed to the flowchart module for guidance. After the case is solved, it can be captured as a new example to be acquired by the connectionist module. Besides providing an economical way for developing fault diagnostic systems in general, the learning process of the system highly resembles the way an expert acquires knowledge through experience