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Residual Generation (residual + generation)
Selected AbstractsFault diagnosis of a simulated industrial gas turbine via identification approachINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2007S. Simani Abstract In this paper, a model-based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors-in-variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single-shaft industrial gas turbine MATLAB/SIMULINK® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte-Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd. [source] All linear methods are equal,and extendible to (some) nonlinearitiesINTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL, Issue 8 2002Janos GertlerArticle first published online: 17 JUN 200 Abstract Several linear methods of residual generation for fault detection and diagnosis are reviewed. The parity relation approach is introduced in some detail, for both additive and parametric faults. The Chow,Willsky scheme, various diagnostic observers and principal component analysis are compared to the additive version. The ,local approach' and the least-squares estimation of parameter changes are shown to be related to the parametric variant. Nonlinear extensions are demonstrated for all the techniques under additive faults. Copyright © 2002 John Wiley & Sons, Ltd. [source] Fault detection and isolation in robotic manipulators via neural networks: A comparison among three architectures for residual analysisJOURNAL OF FIELD ROBOTICS (FORMERLY JOURNAL OF ROBOTIC SYSTEMS), Issue 7 2001Marco Henrique Terra In this article we discuss artificial neural networks-based fault detection and isolation (FDI) applications for robotic manipulators. The artificial neural networks (ANNs) are used for both residual generation and residual analysis. A multilayer perceptron (MLP) is employed to reproduce the dynamics of the robotic manipulator. Its outputs are compared with actual position and velocity measurements, generating the so-called residual vector. The residuals, when properly analyzed, provides an indication of the status of the robot (normal or faulty operation). Three ANNs architectures are employed in the residual analysis. The first is a radial basis function network (RBFN) which uses the residuals of position and velocity to perform fault identification. The second is again an RBFN, except that it uses only the velocity residuals. The third is an MLP which also performs fault identification utilizing only the velocity residuals. The MLP is trained with the classical back-propagation algorithm and the RBFN is trained with a Kohonen self-organizing map (KSOM). We validate the concepts discussed in a thorough simulation study of a Puma 560 and with experimental results with a 3-joint planar manipulator. © 2001 John Wiley & Sons, Inc. [source] Dynamic state reconciliation and model-based fault detection for chemical processesASIA-PACIFIC JOURNAL OF CHEMICAL ENGINEERING, Issue 6 2009Nelly Olivier-Maget Abstract In this paper, we present a method for the fault detection based on the residual generation. The main idea is to reconstruct the outputs of the system from the measurements using the extended Kalman filter. The estimations are compared to the values of the reference model and so, deviations are interpreted as possible faults. The reference model is simulated by the dynamic hybrid simulator, PrODHyS. The use of this method is illustrated through an application in the field of chemical processes. Copyright © 2009 Curtin University of Technology and John Wiley & Sons, Ltd. [source] | ||||||||||