Diagnostic model processor: Using deep knowledge for process fault diagnosis

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Abstract

Many recent attempts to use expert systems for process fault diagnosis have included information derived from deep knowledge. This information is generally implemented as a rule-based expert system. Drawbacks of this architecture are a lack of generality, poor handling of novel situations, and a lack of transparency. An algorithm called the diagnostic model processor is introduced; it uses the satisfaction of model equations from process plants to arrive at the most likely fault condition. The method is generalized by the process model and diagnostic methodology being separated. The architecture addresses each of the shortcomings discussed. Experiments show that the methodology is capable of correctly identifying fault situations. Furthermore, information is derived from an a priori analysis technique, which is used to show the degree to which different faults can be discriminated based on the model equations available. The results of this analysis add further insight into the diagnoses provided by the diagnostic model processor.

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