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Articles 1 - 4 of 4
Full-Text Articles in Engineering
Kalman Filtering With State Constraints: A Survey Of Linear And Nonlinear Algorithms, Daniel J. Simon
Kalman Filtering With State Constraints: A Survey Of Linear And Nonlinear Algorithms, Daniel J. Simon
Electrical and Computer Engineering Faculty Publications
The Kalman filter is the minimum-variance state estimator for linear dynamic systems with Gaussian noise. Even if the noise is non-Gaussian, the Kalman filter is the best linear estimator. For nonlinear systems it is not possible, in general, to derive the optimal state estimator in closed form, but various modifications of the Kalman filter can be used to estimate the state. These modifications include the extended Kalman filter, the unscented Kalman filter, and the particle filter. Although the Kalman filter and its modifications are powerful tools for state estimation, we might have information about a system that the Kalman filter …
Analytic Confusion Matrix Bounds For Fault Detection And Isolation Using A Sum-Of-Squared-Residuals Approach, Daniel J. Simon, Donald L. Simon
Analytic Confusion Matrix Bounds For Fault Detection And Isolation Using A Sum-Of-Squared-Residuals Approach, Daniel J. Simon, Donald L. Simon
Electrical and Computer Engineering Faculty Publications
Given a system which can fail in 1 of n different ways, a fault detection and isolation (FDI) algorithm uses sensor data to determine which fault is the most likely to have occurred. The effectiveness of an FDI algorithm can be quantified by a confusion matrix, also called a diagnosis probability matrix, which indicates the probability that each fault is isolated given that each fault has occurred. Confusion matrices are often generated with simulation data, particularly for complex systems. In this paper, we perform FDI using sum-of-squared residuals (SSRs). We assume that the sensor residuals are s-independent and Gaussian, which …
Modeling Disk Cracks In Rotors By Utilizing Speed Dependent Eccentricity, Andrew L. Gyekenyesi, Jerzy T. Sawicki, Wayne C. Haase
Modeling Disk Cracks In Rotors By Utilizing Speed Dependent Eccentricity, Andrew L. Gyekenyesi, Jerzy T. Sawicki, Wayne C. Haase
Mechanical Engineering Faculty Publications
This paper discusses the feasibility of vibration-based structural health monitoring for detecting disk cracks in rotor systems. The approach of interest assumes that a crack located on a rotating disk causes a minute change in the system’s center of mass due to the centrifugal force induced opening of the crack. The center of mass shift is expected to reveal itself in the vibration vector (i.e., whirl response; plotted as amplitude and phase versus speed) gathered during a spin-up and/or spin-down test. Here, analysis is accomplished by modeling a Jeffcott rotor that is characterized by analytical, numerical, and experimental data. The …
Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon
Constrained Kalman Filtering Via Density Function Truncation For Turbofan Engine Health Estimation, Daniel J. Simon, Donald L. Simon
Electrical and Computer Engineering Faculty Publications
Kalman filters are often used to estimate the state variables of a dynamic system. However, in the application of Kalman filters some known signal information is often either ignored or dealt with heuristically. For instance, state variable constraints (which may be based on physical considerations) are often neglected because they do not fit easily into the structure of the Kalman filter. This article develops an analytic method of incorporating state variable inequality constraints in the Kalman filter. The resultant filter truncates the probability density function (PDF) of the Kalman filter estimate at the known constraints and then computes the constrained …