On optimization of sensor selection for aircraft gas turbine engines

Many science and management problems can be formulated as global optimization problems. Conventional optimization methods that make use of derivatives and gradients are not, in general, able to locate or identify the global optimum. Sometimes these problems can be solved using exact methods like brute force. Unfortunately, these methods become computationally intractable because of multidimensional search spaces. Hence, the application of heuristics for a class of problems that incorporates knowledge about the problem helps solve optimization problems. Optimization of sensor selection using heuristics can lead to significant improvements in the controllability and observability of a dynamic system. The aim of this research is to investigate optimal or alternate measurement sets for the problem of aircraft gas turbine engine health parameter estimation. The performance metric is defined as a function of the steady state error covariance and the cost of the selected sensors.