Data-driven design of fault detection and isolation systems subject to Hammerstein nonlinearity

• Published in: 2015 American Control Conference (ACC) pp. 214 - 219
• Main Authors: Yulei Wang, Bingzhao Gao, Hong Chen
• Format: Conference Proceeding
• Language: English
• Published: American Automatic Control Council 01.07.2015
• Subjects: Benchmark testing; Convex functions; Covariance matrices; Fault detection; Generators; Linear systems; Permanent magnet motors
• ISSN: 0743-1619
• DOI: 10.1109/ACC.2015.7170738

This paper is concerned with data-driven design of fault detection and isolation (FDI) systems subject to Hammerstein nonlinearity, a static nonlinearity in the front of inputs. Specifically, the design of residual generation is then formulated as to solve a convex optimization problem by combining ideas from the over-parameterization and least squares support vector machines (LS-SVMs), and thus provides residual signals directly from process data. To solve the multiply-outputs (MOs) problem, a modified approach is proposed by means of the so-called mixed block Hankel matrices. Sufficient conditions for the existence of a parity space are established and proved. A benchmark example is given to show the effectiveness of the proposed approach.