Master of Science, The Ohio State University, 2020, Mechanical Engineering

The EcoCAR Mobility Challenge is the current iteration of the Advanced Vehicle Technology Competitions that challenges twelve universities across North America to re-engineer a 2019 Chevrolet Blazer into a connected and automated vehicle. The competition goal is to design, prototype, test, and validate a SAE Level 2 advance driver assistance system. This work outlines the development process of a SAE Level 2 perception system. The process began by defining system and component level requirements that iniated a sophisticated sensor and hardware selection process. Then to protoype, test, and validate the system, a V-model approach was followed, which included validation and verification of the system in multiple test environments. The role of each test environment in the validation process along with its advantages and shortcomings is discussed in detail, followed by the evolution of the perception system throughout Year 1 and Year 2 of the competition. Next, three case studies outlining the different subsystems in the perception controller: the I/O layer, the fault diagnostics, and sensor calbration are discussed. Each of these sub-algorithms used various modeling environment to increase the realiability and accuracy of the perception system. This work serves as the foundation of the connected and automated vehicle perception system and will be vital in the implementation of advance driver assistance features such as adaptive cruise control, lane centering control, and lane change on demand in future years of this competition.

Committee: Shawn Midlam-Mohler (Advisor); Lisa Fiorentini (Committee Member); Punit Tulpule (Other) Subjects: Automotive Engineering; Mechanical Engineering

MS, University of Cincinnati, 2011, Engineering and Applied Science: Aerospace Engineering

Current diagnostics on most gas turbine engines involve off-line processing only. Since failures can cause serious safety and efficiency problems, such as elevated turbine temperatures or compressor stall, it is desirable to diagnose problems in as close to real-time as possible. This project applies some of the methodology of Rausch, et. al. to a simulation of a low bypass turbofan. The model uses 9 health parameters to simulate faults or degradation of engine components. Sensor residuals from an extended Kalman filter were used with a non-linear engine model to estimate the engine health parameters. Other methods for generating health parameter estimates were also implemented and compared, including a tracking filter based on Newton's method and a back-propagation neural network. An implementation of a Bayesian network to engine fault diagnostics is demonstrated and a fuzzy diagnostic system is developed using a similar method, avoiding many of the difficulties traditionally encountered while developing fuzzy systems (the effectively infinite design degrees of freedom available while designing the system). Finally, the results of the diagnostic systems are compared in terms of accuracy of fault diagnosed, accuracy of the health parameter estimates produced, (simulation) time taken to produce a correct diagnosis, and time needed for the computation. The Bayesian network and fuzzy system have the best overall performance: both systems correctly diagnose each component fault, while the LKF and tracking filter fail for some cases and the neural network fails under some conditions. The Bayesian network diagnoses faults in about half the time from the introduction of the fault, while the fuzzy system estimates the health parameters more accurately and is less computationally intensive.

Committee: Bruce Walker ScD (Committee Chair); Kelly Cohen PhD (Committee Member); Daniel Humpert MS (Committee Member) Subjects: Aerospace Materials

Doctor of Philosophy, The Ohio State University, 2010, Aero/Astro Engineering

Fault detection and isolation plays a critical role in aircraft engines and the performance of their control systems. A great amount of research on model-based fault detection and isolation of aircraft engines has been studied since the 1970s. Model-based fault detection and isolation methods rely on the accuracy of the model. Model uncertainty, disturbances and noise, etc., all have a great impact on the fault detection and isolation design results. A challenge in the fault detection applications is the design of a scheme which can distinguish between model uncertainties, disturbances and the occurrence of faults. Most of the current approaches use a constant detection threshold. Currently, there are no useful guidelines for constant optimal threshold selection. In the absence of faults, a predetermined constant threshold would lead to more false alarms and missed detections under modeling uncertainties. Hence a technique to accommodate uncertainties and disturbances in the model, help in reducing false alarms and missed detections is essential for the enhancement of aircraft engine operations. In this work, a dynamic threshold algorithm is developed for aircraft engine fault detection and isolation that accommodates parametric uncertainties and disturbances. The algorithm takes the parametric uncertainties into consideration and proposes a dynamic threshold that makes use of the bounds on the parametric uncertainties which can thus distinguish an actual fault from the model uncertainties. First we design Kalman filters or unknown input observers based on the linearized engine model about a given nominal operating point, but the filters or observers use the measurements from the nonlinear engine model which includes uncertainty description. Using the robustness analysis of parametric uncertain systems, we generate upper-bound and lower-bound time response trajectories of the dynamic threshold. The extent of parametric uncertainties is assumed to be such that the pe (open full item for complete abstract)

Committee: Rama Yedavalli PhD (Advisor); Jen-Ping Chen PhD (Committee Member); Mo-how Herman Shen PhD (Committee Member) Subjects: Engineering