Policy search for active fault diagnosis with partially observable state

Abstract

Summary The article deals with a novel design of an active fault detector (AFD) for a nonlinear stochastic system with a partially observable state. The imperfect state information problem is converted to a perfect state information problem using a state estimator. Subsequently, the problem is decomposed into separate tasks of an optimal fault detector design and an approximate input generator design using a dynamic programming technique. While the former task is straightforward, the latter represents a nonlinear functional optimization problem. The input generator is approximated by a multi‐layer perceptron neural network, and its unknown parameters are found using the policy search method. Effectiveness of the proposed AFD design is demonstrated numerically on a pendulum system and a heating/cooling system.