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Semi-supervised Approach to Soft Sensor Modeling for Fault Detection in Industrial Systems with Multiple Operation Modes
In industrial systems, certain process variables that need to be monitored
for detecting faults are often difficult or impossible to measure. Soft sensor
techniques are widely used to estimate such difficult-to-measure process
variables from easy-to-measure ones. Soft sensor modeling requires training
datasets including the information of various states such as operation modes,
but the fault dataset with the target variable is insufficient as the training
dataset. This paper describes a semi-supervised approach to soft sensor
modeling to incorporate an incomplete dataset without the target variable in
the training dataset. To incorporate the incomplete dataset, we consider the
properties of processes at transition points between operation modes in the
system. The regression coefficients of the operation modes are estimated under
constraint conditions obtained from the information on the mode transitions. In
a case study, this constrained soft sensor modeling was used to predict
refrigerant leaks in air-conditioning systems with heating and cooling
operation modes. The results show that this modeling method is promising for
soft sensors in a system with multiple operation modes.Comment: 7 pages, 1 figur