Orthotopic-filtering-based fault diagnosis algorithms for nonlinear systems with slowly varying faults

A novel nonlinear fault diagnosis method for detecting various slowly varying faults is proposed. The idea of linear programming binding constraints is adopted and the orthotopic spaces are recursively calculated to approximate the exact feasible parameter set, and a parameter global expansion filtering fault diagnosis algorithm is derived. Moreover, to reduce the amount of calculation and improve the efficiency of fault diagnosis, a parameter directional expansion filtering fault diagnosis algorithm is proposed based on the spatial dimension reduction. Finally, various simulation examples are provided to demonstrate the accuracy and effectiveness of the proposed algorithms. (C) 2020 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.