Andere Kunden interessierten sich auch für
![Robust Nonlinear Fault Detection and Isolation of Robotic System]( "Robust Nonlinear Fault Detection and Isolation of Robotic System")
Robust Nonlinear Fault Detection and Isolation of Robotic System38,99 €![Tomographic Reconstruction of Label Images Using Gibbs Priors]( "Tomographic Reconstruction of Label Images Using Gibbs Priors")
Tomographic Reconstruction of Label Images Using Gibbs Priors44,99 €![Modeling & Fault Diagnosis of IM by using Intelligent Control]( "Modeling & Fault Diagnosis of IM by using Intelligent Control")
Modeling & Fault Diagnosis of IM by using Intelligent Control51,99 €![Under Water Coral Reef Images 3D Reconstruction Technique]( "Under Water Coral Reef Images 3D Reconstruction Technique")
Under Water Coral Reef Images 3D Reconstruction Technique36,99 €![Evaluation of Rubber Pad for Vibration Isolation]( "Evaluation of Rubber Pad for Vibration Isolation")
Evaluation of Rubber Pad for Vibration Isolation29,99 €![Design of Base Isolation Systems for an R.C.C. Building]( "Design of Base Isolation Systems for an R.C.C. Building")
Design of Base Isolation Systems for an R.C.C. Building44,99 €![Microtools for microsurgery and reconstruction of a single cell]( "Microtools for microsurgery and reconstruction of a single cell")
Microtools for microsurgery and reconstruction of a single cell24,99 €
Process history based approaches for fault diagnosis has been widely used recently. Principal Component Analysis (PCA) is one of these approaches, which is a linear approach; however most of the processes are nonlinear. Hence nonlinear extensions of the PCA have been developed. Nonlinear Principal Component Analysis (NLPCA) based on the neural networks is a common method which is used for process monitoring and fault diagnosis. NLPCA based neural networks are implemented using different methods, in this book we apply Auto-Associative Neural Networks (AANN) for implementing NLPCA. This work is aimed towards the development of an algorithm used in conjunction with an Auto Associative Neural Network (AANN) to help locate and reconstruct faulty sensor inputs in control systems. Also an algorithm is developed for locating the source of the process fault.