Satnam Alag

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

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.

A methodology for intelligent sensor validation and fusion used in tracking and avoidance of objects for automated vehicles

For longitudinal control the automated vehicles in intelligent vehicle highway system (IVHS) require sensors to estimate the relative distance and velocity between vehicles. High data fidelity of these sensors is required to maintain the reliability and safety of the IVHS. In this paper, the authors develop a methodology for validation and fusion of sensory readings obtained from multiple sensors used for tracking automated vehicles and for avoiding objects in its path. The authors introduce tracking models for the various operating states of the automated vehicle, namely vehicle following, maneuvering, i.e. split, merge, lane change, emergencies, and for the lead vehicle in a platoon. The Kalman filtering approach is proposed for the formation of real time validation gates. This along with the algorithmic sensor validation filter is used for sensory data validation. The validated data are then fused by using a Bayesian method called the probabilistic data association filter. The procedure is demonstrated by two examples using simulated data, data obtained from a platooning test set-up.