Stochastic Qualitative Model Generation for Diagnosis for Building Air Conditioning Systems

Abstract

In recent building air conditioning systems, it is difficult to clarify qualitative physical relationships among components of the system because of diversification and limited sensor points. In the stochastic qualitative reasoning, which we have proposed, fault diagnosis is performed with the stochastic qualitative model which represents the behavior of a target system with probabilities. However, the probability tuning process in the model is the most difficult and important task in the model construction.
This paper proposes an approach to automatic model generation to cope with this problem. Firstly, propagation rules and functions are formalized with several characteristic parameters. And it is confirmed that the agreement rate, which represents the degree of agreement of the simulation with the measured behavior, is smoothly changed according to the change of parameters. Then, based on the tentative model, which is constructed from a target system design, the reasonable qualitative model is generated with measured field data by using the characteristic parameter tuning method.
This method has applied to a real building air conditioning system. As a result, We can generate a stochastic qualitative model which expresses the normal behavior of the target system.