  Early fault detection using instrumented sensor data is one of the promising
application areas of machine learning in industrial facilities. However, it is
difficult to improve the generalization performance of the trained
fault-detection model because of the complex system configuration in the target
diagnostic system and insufficient fault data. It is not trivial to apply the
trained model to other systems. Here we propose a fault diagnosis method for
refrigerant leak detection considering the physical modeling and control
mechanism of an air-conditioning system. We derive a useful scaling law related
to refrigerant leak. If the control mechanism is the same, the model can be
applied to other air-conditioning systems irrespective of the system
configuration. Small-scale off-line fault test data obtained in a laboratory
are applied to estimate the scaling exponent. We evaluate the proposed scaling
law by using real-world data. Based on a statistical hypothesis test of the
interaction between two groups, we show that the scaling exponents of different
air-conditioning systems are equivalent. In addition, we estimated the time
series of the degree of leakage of real process data based on the scaling law
and confirmed that the proposed method is promising for early leak detection
through comparison with assessment by experts.
