Transformation of Quantitative Measurements into Qualitative Values in Stochastic Qualitative Reasoning for Fault Detection

Takahiro Yamasaki, Masaki Yumoto, Takenao Ohkawa Norihisa Komoda and Fusachika Miyasaka

Fault detection by stochastic qualitative reasoning is an effective way for complex systems such as air conditioning systems. In this framework, the faulty part of a system can be identified by comparing the behavior derived by stochastic qualitative reasoning with the actual measured behavior. The latter is represented as the series of qualitative values that are obtained by classifying quantitative measurements into several qualitative categories based on a definition of the qualitative regions. The fault detection is often ineffective under the inappropriate definitions.

This paper proposes a method that can automatically define the qualitative regions from the measured data. In this system, data are controlled using a certain value and follow a normal distribution. Measurement data must be transformed into stable qualitative values so that its behavior can be distinguished from fault conditions: therefore, the middle of the qualitative region which has the most stable qualitative value is determined as the average value of the data. The width of the most stable qualitative value is determined based on the standard deviation.

This method is applied to an actual air conditioning system. According to the definition of qualitative regions that is determined from the field data, the faults can be identified.

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