A methodology for intelligent sensor measurement, validation, fusion, and fault detection for equipment monitoring and diagnostics

Authors:
Satnam Alag;Alice M. Agogino;Mahesh Morjaria
Affiliations:
Gazoo Corporation;Department of Mechanical Engineering, University of California at Berkeley, Berkeley CA 94720, USA;GE Energy Services, 4200 Wildwood Parkway, Atlanta, GA 30339, USA
Venue:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year:
2001

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Abstract

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.