Qualitative physics gives good behavioral description for systems with extremely arnbiguous information. However, in engineering applications less ambiguous information is generally available which can be represented easily in terms of interval numbers. In this work, the interval arithmetic reasoner (IAR) is proposed to deal with the interval types of confluences. A resolving technique is discussed to find a less ambiguous solution for interval model equations. If the intervals describing physical quantities approach infinity, then the current method approaches to the ENVISION system. On the other hand, if each physical quantity is a crisp number, then the IAR becomes conventional quantitative
Equation types of deep models are often employed in fault diagnosis. Upon diagnosis this quantitative process knowledge is utilized as a criterion for satisfaction/violation in a Boolean or non-Boolean manner. Therefore, the resolution of equation-oriented fault diagnosis systems is often limited to, a t most, fault isolation a t a qualitative level. A deep model algorithm (DMA) is proposed to improve diagnostic resolution. First, tolerances of model equations are defined for each model equation with respect to each fault origin. Following the new definition of tolerance, degree of fault is defined to detect the level of fault and a consistency factor is used to evaluate the consistency given by different model equations. A CSTR example is used to illustrate the resolution of DMA. Resultsshow that the proposed method is effective in identifying fault origins.
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