Design of active inputs for set-based fault diagnosis

Effective fault diagnosis depends on the detectability of the faults in the measurements, which can be improved by a suitable input signal. This article presents a deterministic method for computing the set of inputs that guarantee fault diagnosis, referred to as separating inputs. The process of interest is described, under nominal and various faulty conditions, by linear discrete-time models subject to bounded process and measurement noise. It is shown that the set of separating inputs can be efficiently computed in terms of the complement of one or several zonotopes, depending on the number of fault models. In practice, it is essential to choose elements from this set that are minimally harmful with respect to other control objectives. It is shown that this can be done efficiently through the solution of a mixed-integer quadratic program. The method is demonstrated for a numerical example.

[1]  José Fortuny-Amat,et al.  A Representation and Economic Interpretation of a Two-Level Programming Problem , 1981 .

[2]  Xue Jun Zhang,et al.  Auxiliary Signal Design in Fault Detection and Diagnosis , 1989 .

[3]  Ramine Nikoukhah,et al.  Guaranteed Active Failure Detection and Isolation for Linear Dynamical Systems , 1998, Autom..

[4]  François Delebecque,et al.  Detection signal design for failure detection: a robust approach , 2000 .

[5]  T. F. Lootsma,et al.  Observer-based Fault Detection and Isolation for Nonlinear Systems , 2001 .

[6]  Kesa,et al.  A set based approach to detection and isolation of faults in multivariable systems , 2001 .

[7]  Stephen L. Campbell,et al.  Auxiliary signal design for rapid multi-model identification using optimization , 2002, Autom..

[8]  Thomas J. McAvoy,et al.  Fault Detection and Diagnosis in Industrial Systems , 2002 .

[9]  Leonidas J. Guibas,et al.  Zonotopes as bounding volumes , 2003, SODA '03.

[10]  Raghunathan Rengaswamy,et al.  A review of process fault detection and diagnosis: Part I: Quantitative model-based methods , 2003, Comput. Chem. Eng..

[11]  M. V. Iordache,et al.  Diagnosis and Fault-Tolerant Control , 2007, IEEE Transactions on Automatic Control.

[12]  Ramine Nikoukhah,et al.  Auxiliary Signal Design for Failure Detection , 2004 .

[13]  M. Simandl,et al.  Rolling horizon for active fault detection , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[14]  David G. Kirkpatrick,et al.  Computing the intersection-depth of polyhedra , 1993, Algorithmica.

[15]  C. Combastel A State Bounding Observer for Uncertain Non-linear Continuous-time Systems based on Zonotopes , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[16]  Stephen L. Campbell,et al.  Auxiliary signal design for active failure detection in uncertain linear systems with a priori information , 2006, Autom..

[17]  S.L. Campbell,et al.  Active fault detection in nonlinear systems using auxiliary signals , 2008, 2008 American Control Conference.

[18]  Steven X. Ding,et al.  Model-based Fault Diagnosis Techniques: Design Schemes, Algorithms, and Tools , 2008 .

[19]  Youdong Lin,et al.  Fault Detection in Nonlinear Continuous-Time Systems with Uncertain Parameters , 2008 .

[20]  Vicenç Puig,et al.  Robust fault detection using zonotope‐based set‐membership consistency test , 2009 .

[21]  Inseok Hwang,et al.  A Survey of Fault Detection, Isolation, and Reconfiguration Methods , 2010, IEEE Transactions on Control Systems Technology.

[22]  R. Findeisen,et al.  Complete Fault Diagnosis of Uncertain Polynomial Systems , 2010 .

[23]  O. Stursberg,et al.  Computing Reachable Sets of Hybrid Systems Using a Combination of Zonotopes and Polytopes , 2010 .

[24]  F. Stoican Fault tolerant control based on set-theoretic methods , 2011 .

[25]  Vicenç Puig,et al.  Robust fault detection of non-linear systems using set-membership state estimation based on constraint satisfaction , 2012, Eng. Appl. Artif. Intell..

[26]  Stephen L. Campbell,et al.  Effects of feedback on active fault detection , 2012, Autom..

[27]  Ali Zolghadri,et al.  Advanced model-based FDIR techniques for aerospace systems: Today challenges and opportunities , 2012 .