In equipment monitoring and diagnostics, it is very important
to distinguish between a sensor failure and a system failure.
In this paper, we develop a comprehensive methodology based
on a hybrid system of AI and statistical techniques. The
methodology is designed for monitoring complex equipment systems,
which validates the sensor data, associates a degree of validity
with each measurement, isolates faulty sensors, estimates the
actual values despite faulty measurements, and detects incipient
sensor failures. The methodology consists of four steps: redundancy
creation, state prediction, sensor measurement validation and
fusion, and fault detection through residue change detection.
Through these four steps we use the information that can be
obtained by looking at: information from a sensor individually,
information from the sensor as part of a group of sensors, and
the immediate history of the process that is being monitored.
The advantage of this methodology is that it can detect multiple
sensor failures, both abrupt as well as incipient. It can also
detect subtle sensor failures such as drift in calibration and
degradation of the sensor. The four-step methodology is applied
to data from a gas turbine power plant.