Abstract
Traditional experiential knowledge-based diagnostic systems have suffered in their application to real-world problems due to their inability to handle unanticipated situations. Subsequently, diagnostic reasoning approaches have been developed that rely on a model of the device's normal operation to mitigate these shortcomings; these systems are called model-based diagnostic systems. The models of model-based systems are primarily a representation of the actual components of the device, which each have a defined behavior, along with a description of the topology of the device. The behavior of the device is simulated by introducing values into the device's inputs and propagating their effects throughout the interconnected components. Model-based systems often require an excessive amount of computation and data to arrive at a solution, primarily because information about any component in the model can only be utilized to make additional inferences about the components which are physical proximal to it in the...
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
