Browsing by Subject "Smooth Variable Structure Filter"
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Item Fault Detection Using an Artificial Neural Network and Variable Structure Based Approach(2017-01-01) Sanghyun, Andrew; Gadsden, Stephen A; Mechanical Engineering; Engineering, MechanicalThe ultimate goal of fault detection and isolation is to maximize the life span of equipment and minimize the cost of maintenance. The development of intelligent diagnostic, prognostic, and health management technology has proven to be important for industrial and defense maintenance procedures in recent years. While diagnostic technology for aircraft have existed for more than 50 years, modern CPUs permit on-board intelligent and estimation-based fault detection methods. This theses discussed two strategies in particular: artificial neural networks and smooth variable structure filters. The purpose of this theses is to propose a method of health state awareness for a helicopter blade using an artificial neural network as well as develop a variable structure-based fault detection and diagnosis strategy for an electromechanical actuator.Item A multiple model adaptive SVSF-KF estimation strategy(SPIE, 2019-05-07) Goodman, Jacob M.; Wilkerson, Stephen A.; Eggleton, Charles; Gadsden, S. AndrewState estimation strategies play a critical role in obtaining accurate information about the state of dynamic systems as they develop. Such information can be important on its own and critical for precise and predictable control of such systems. The Kalman filter (KF) is a classic algorithm and among the most powerful tools in state estimation. The Kalman filter however can be sensitive to modeling uncertainty and sudden changes in system dynamics. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy that operates on variable structure concepts. In general, the SVSF has the advantage that is can be quite robust to modeling uncertainty and sudden fault conditions. Recent advancements to the SVSF, such as the addition of a covariance formulation, and the derivation of a time varying smoothing boundary layer (VBL), have allowed for combined SVSF – KF strategies. In a typical SVSF-KF approach, the VBL is used to detect the presence of a system fault, and switch from the more optimal KF gain to the more robust SVSF gain. While this approach has been proven effective in several cases, there are circumstances where the VBL will fail to indicate the presence of an ongoing fault. A new form of the SVSF-KF is proposed, based on the framework of the Multiple Model Adaptive Estimator.Item THE SMOOTH VARIABLE STRUCTURE-KALMAN FILTER: A ROBUST AND OPTIMAL ESTIMATION STRATEGY(2019-01-01) Goodman, Jacob MGoodman, Jacob M; Gadsden, S. Andrew; Eggleton, Charles; Mechanical Engineering; Engineering, MechanicalState estimation strategies are vital for obtaining knowledge of a dynamic system'sstate where one is faced with challenges such as limited measurement capability, sensor noise, and uncertain system dynamics. The Kalman filter (KF), is one of the most popular tools in state estimation and provides the optimal solution for linear state estimation problems. The Smooth Variable Structure Filter (SVSF) is a relatively new estimation strategy based on variable structure theory and sliding mode concepts. Although the SVSF is not an optimal filter it is highly robust to modeling uncertainty and system change. The Smooth Variable Structure Filter ? Kalman Filter (SVSF-KF) is an adaptive estimation algorithm that attempts to provide an optimal KF estimate during normal system operation and the robust SVSF estimate during a fault. The existing SVSF-KF method uses a time varying smoothing boundary layer (VBL) to detect system change and an adaptive gain. This method while effective in some cases, has been shown to suffer several drawbacks. We propose three new approaches for implementing the aim of the SVSF-KF. One, an adaptive gain formulation based on the normalize innovation square, termed the NIS SVSF-KF, and two using multiple model frameworks, termed the MMAE SVSF-KF and IMM SVSF-KF respectively. The new methods are demonstrated via computer experiment on a simple harmonic oscillator scenario and an electro-hydrostatic actuator benchmark case. All three methods show significant improvement over the original SVSF-KF.