Sensor Placement for Fault Isolation in Linear Differential-Algebraic Systems

Abstract An algorithm is proposed for computing which sensor additions that make a diagnosis requirement specification regarding fault detectability and isolability attainable for a given linear differential-algebraic model. Restrictions on possible sensor locations can be given and if the diagnosis specification is not attainable with any available sensor addition, the algorithm provides the solutions that maximize specification fulfillment. Previous approaches with similar objectives have been based on the model structure only. Since the proposed algorithm utilizes the analytical expressions, it can handle models where structural approaches fail. A Mathematica implementation of the algorithm can be downloaded from http://www.fs.isy.liu.se/Software/LinSensPlaceTool/ .

[1]  Christian Commault,et al.  STRUCTURAL ANALYSIS FOR THE SENSOR LOCATION PROBLEM IN FAULT DETECTION AND ISOLATION , 2006 .

[2]  Erik Frisk,et al.  Residual Generation for Fault Diagnosis of Systems Described by Linear Differential-Algebraic Equations , 2006, IEEE Transactions on Automatic Control.

[3]  M. Nyberg.  Criterions for detectability and strong detectability of faults in linear systems , 2000 .

[4]  Teresa Escobet,et al.  Diagnosability Analysis Based on Component-Supported Analytical Redundancy Relations , 2006, IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans.

[5]  Stéphane Lafortune,et al.  On an Optimization Problem in Sensor Selection* , 2002, Discret. Event Dyn. Syst..

[6]  Raymond Reiter,et al.  A Theory of Diagnosis from First Principles , 1986, Artif. Intell..

[7]  Brian C. Williams,et al.  Diagnosing Multiple Faults , 1987, Artif. Intell..

[8]  Erik Frisk,et al.  Sensor placement for maximum fault isolability , 2007 .

[9]  Marcel Staroswiecki,et al.  Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[10]  Michael Sebek,et al.  An algorithm for polynomial matrix factor extraction , 2000 .

[11]  A. Benveniste,et al.  Optimal sensor location for detecting changes in dynamical behavior , 1986, 1986 25th IEEE Conference on Decision and Control.

[12]  Hui Wang,et al.  Statistical process monitoring using improved PCA with optimized sensor locations , 2002 .

[13]  Raghunathan Rengaswamy,et al.  Locating sensors in complex chemical plants based on fault diagnostic observability criteria , 1999 .