Model‐Based Failure Detection on Nonlinear Systems: Theory and Transition

Abstract

Failure detection is an active area of Navy research due to its many important applications. Recently, an approach for multi-model identification and fault detection in the presence of bounded energy noise over finite time intervals has been introduced. This family of algorithms was originally designed to work on linear systems that can be modeled analytically. In this paper, efforts made toward extending this algorithm for fault detection to nonlinear systems along with efforts in testing this family of algorithms on real systems are discussed.

Multi-model estimation using neural network and fault detection in unknown time continuous fractional order nonlinear systems

“…In Shumsky (1995), fault detection problem in unknown nonlinear systems based on reduced order nonlinear observer has been proposed. In Drake et al (2007), an algorithm has been presented for model-based fault detection for nonlinear systems. In Chaib et al (2009), fault detection and restoration in switched nonlinear systems has been presented.…”