  The validation of data from sensors has become an important issue in the
operation and control of modern industrial plants. One approach is to use
knowledge based techniques to detect inconsistencies in measured data. This
article presents a probabilistic model for the detection of such
inconsistencies. Based on probability propagation, this method is able to find
the existence of a possible fault among the set of sensors. That is, if an
error exists, many sensors present an apparent fault due to the propagation
from the sensor(s) with a real fault. So the fault detection mechanism can only
tell if a sensor has a potential fault, but it can not tell if the fault is
real or apparent. So the central problem is to develop a theory, and then an
algorithm, for distinguishing real and apparent faults, given that one or more
sensors can fail at the same time. This article then, presents an approach
based on two levels: (i) probabilistic reasoning, to detect a potential fault,
and (ii) constraint management, to distinguish the real fault from the apparent
ones. The proposed approach is exemplified by applying it to a power plant
model.
