Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
HUGIN: a shell for building Bayesian belief universes for expert systems
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Hidden non-Markovian reward models: virtual stochastic sensors for hybrid systems
Proceedings of the Winter Simulation Conference
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Modern control systems and other monitoring systems require the acquisition of values of most of the parameters involved in the process. Examples of processes are industrial procedures or medical treatments or financial forecasts. However, sometimes some parameters are inaccessible through the use of traditional instrumentation. One example is the blades temperature in a gas turbine during operation. Other parameters require costly instrumentation difficult to install, operate and calibrate. For example, the contaminant emissions of power plant chimney. One solution of this problem is the use of analytical estimation of the parameter using complex differential equations. However, these models sometimes are very difficult to obtain and to maintain according the changes in the processes. Other solution is to borrow an instrument and measure a data set with the value of the difficult variable and its related variables at all the operation range. Then, use an automatic learning algorithm that allows inferring the difficult measure, given the related variables. This paper presents the use of Bayesian networks that represents the probabilistic relations of all the variables in a process, in the design of a virtual sensor. Experiments are presented with the temperature sensors of a gas turbine.