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Networksby Owen MACMANN Good prognostic health management (PHM) solutions for jet engines remain elusive, owing partially to lack of run-to-failure data sets. A good PHM solution has the potential to improve on unscheduled maintenance by offering an accurate, real-time estimation of the engine's current health state. Aero-engine simulations allow for generation of simulated data invaluable for data-driven PHM solutions. Simulated data can characteristically represent propagation of faults in an engine over time and present the results of that fault propagation in terms of realistically acquirable sensor data. A method of data set generation for jet engine degradation that incorporates multiple faults is described. The generated data sets can be used for training a combined diagnostic/prognostic solution. This work proposes a neural network-based prognostic system that uses diagnostic evaluations as additional tag data for a prognostic analysis.Self-organized maps are used to classify data. The classifications are added to the data as an additional input for a neural network designed to predict remaining usable life.The method exhibits totally autonomous learning of data and produces improvements over approaches that do not pre-classify data.iii The author of this work would like to thank several individuals for their assistance in realizing all that is laid here before you: Dr. Kelly Cohen, for his unfailing support and good attitude; Dr. Alireza Behbahani of the Air Force Research Laboratory, for his team's help in providing software, literature, and insights crucial to understanding C-MAPSS40K and the larger problems behind diagnostics and prognostics; and, Dr.Kristin Rozier, for her help in providing a foundational understanding of mathematical logic.ix

This paper describes a fuzzy logic controller for fault tolerant control of nonlinear flat systems. The control hybridizes flat reference parameters with a fuzzy logic control regulator to achieve robustness against sensing and effector faults for control of a three-tank system. Two methods of fault tolerant control are presented. One method is passive in that although a control reconfiguration is applied, it is not explicitly attached to the detection of any faults and instead applies a general principle to the problem class. This method applies only a fuzzy logic control regulator to a flatness-derived reference signal. The second method is active and applies a control reconfiguration based on the analysis of residuals taken from the difference between the measured signals and the flat system signals. The feasibility of these approaches are verified for additive and multiplicative faults in a three-tank system.

In this study, a novel solution for automated tracking of multiple unknown aircraft is proposed. Many current methods use transponders to self-report state information and augment track identification. While conformant aircraft typically report transponder information to alert surrounding aircraft of its state, vehicles may exist in the airspace that are non-compliant and need to be accurately tracked using alternative methods. In this study, a multi-agent tracking solution is presented that solely utilizes primary surveillance radar data to estimate aircraft state information. Main research challenges include state estimation, track management, data association, and establishing persistent track validity. In an effort to realize these challenges, techniques such as Maximum a Posteriori estimation, Kalman filtering, degree of membership data association, and Nearest Neighbor Spanning Tree clustering are implemented for this application.