Real time intelligent sensor validation

Abstract

The validation of data from sensors has become an important issue in the operation and control of modern power plants. One approach is to use knowledge based techniques to detect inconsistencies in measured data. These techniques involve two challenges: real time performance and the use of reasoning methods under uncertainty. This article presents an algorithm for intelligent sensor validation in real time environments. The algorithm utilizes a Bayesian network for the detection of a fault in a set of sensors. This Bayesian network represents the dependencies and independencies among all the sensors. A second Bayesian network isolates the faulty sensor among all the apparent faulty sensors. This isolation is made incrementally, i.e., a probability of failure vector is provided at any time and the quality of the belief measurements increases when more time is spent in the computation. This characteristic makes the algorithm suitable for use in real time environments. An empirical evaluation is presented in the validation of temperature sensors of a gas turbine in a combined cycle plant in México.