KEYWORDS: Instrument modeling, Model-based design, Telescopes, Space telescopes, Transition metals, Temperature metrology, Sensors, Temperature sensors, Hubble Space Telescope, Astatine
Very rarely do physical devices function as intended by their designers the first time they are implemented. Usually, there are two ways in which the behavior of a device may deviate from the intended one: the device may not exhibit a desired behavior or it may result in an undesirable behavior. We describe a model-based method for solving the latter task. This method involves diagnosis and repair of the failed device and verification of the modified device. It uses compiled structure-behavior-function (SBF) models of how the device works. In an SBF model, the behaviors and the structural elements of a device act as indices into causal mechanisms that explain how the structure of the device produces its behaviors. The causal mechanisms in turn serve as indices into qualitative relations between device variables. The KRITIK2 system uses this indexing scheme to access relevant causal mechanisms and qualitative relations, and uses this knowledge for solving the diagnosis, repair, and verification subtasks of redesign. KRITIK2 shows that this model-based method is sufficient for parametric redesign even for devices in which a single cause results in multiple effects and a single structural element plays a role in multiple causal behaviors.
Designing a novel class of devices requires innovation. Often, the design knowledge of these devices does not identify and address the constraints that are required for their performance in the real world operating environment. So any new design adapted from these devices tend to be similarly sketchy. In order to address this problem, we propose a case-based reasoning method called performance driven innovation (PDI). We model the design as a dynamic process, arrive at a design by adaptation from the known designs, generate failures for this design for some new constraints, and then use this failure knowledge to generate the required design knowledge for the new constraints. In this paper, we discuss two aspects of PDI: the representation of PDI cases and the translation of the failure knowledge into design knowledge for a constraint. Each case in PDI has two components: design and failure knowledge. Both of them are represented using a substance-behavior-function model. Failure knowledge has internal device failure behaviors and external environmental behaviors. The environmental behavior, for a constraint, interacting with the design behaviors, results in the failure internal behavior. The failure adaptation strategy generates functions, from the failure knowledge, which can be addressed using the routine design methods. These ideas are illustrated using a coffee-maker example.
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Visual Analytics for Homeland Defense and Security
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