A Bayesian Approach to Sensor Placement and System Health Monitoring

System health monitoring and sensor placement are areas of great technical and scientific interest. Prognostics and health management of a complex system require multiple sensors to extract required information from the sensed environment, because no single sensor can obtain all the required information reliably at all times. The increasing costs of aging systems and infrastructures have become a major concern, and system health monitoring techniques can ensure increased safety and reliability of these systems. Similar concerns also exist for newly designed systems. The main objectives of this research were: (1) to find an effective way for optimal functional sensor placement under uncertainty, and (2) to develop a system health monitoring approach with both prognostic and diagnostic capabilities with limited and uncertain information sensing and monitoring points. This dissertation provides a functional/information --based sensor placement methodology for monitoring the health (state of reliability) of a system and utilizes it in a new system health monitoring approach. The developed sensor placement method is based on Bayesian techniques and is capable of functional sensor placement under uncertainty. It takes into account the uncertainty inherent in characteristics of sensors as well. It uses Bayesian networks for modeling and reasoning the uncertainties as well as for updating the state of knowledge for unknowns of interest and utilizes information metrics for sensor placement based on the amount of information each possible sensor placement scenario provides. A new system health monitoring methodology is also developed which is: (1) capable of assessing current state of a system's health and can predict the remaining life of the system (prognosis), and (2) through appropriate data processing and interpretation can point to elements of the system that have or are likely to cause system failure or degradation (diagnosis). It can also be set up as a dynamic monitoring system such that through consecutive time steps, the system sensors perform observations and send data to the Bayesian network for continuous health assessment. The proposed methodology is designed to answer important questions such as how to infer the health of a system based on limited number of monitoring points at certain subsystems (upward propagation); how to infer the health of a subsystem based on knowledge of the health of the main system (downward propagation); and how to infer the health of a subsystem based on knowledge of the health of other subsystems (distributed propagation)

Testing for delay defects utilizing test data compression techniques

textAs technology shrinks new types of defects are being discovered and new fault models are being created for those defects. Transition delay and path delay fault models are two such models that have been created, but they still fall short in that they are unable to obtain a high test coverage of smaller delay defects; these defects can cause functional behavior to fail and also indicate potential reliability issues. The first part of this dissertation addresses these problems by presenting an enhanced timing-based delay fault testing technique that incorporates the use of standard delay ATPG, along with timing information gathered from standard static timing analysis. Utilizing delay fault patterns typically increases the test data volume by 3-5X when compared to stuck-at patterns. Combined with the increase in test data volume associated with the increase in gate count that typically accompanies the miniaturization of technology, this adds up to a very large increase in test data volume that directly affect test time and thus the manufacturing cost. The second part of this dissertation presents a technique for improving test compression and reducing test data volume by using multiple expansion ratios while determining the configuration of the scan chains for each of the expansion ratios using a dependency analysis procedure that accounts for structural dependencies as well as free variable dependencies to improve the probability of detecting faults. Finally, this dissertation addresses the problem of unknown values (X’s) in the output response data corrupting the data and degrading the performance of the output response compactor and thus the overall amount of test compression. Four techniques are presented that focus on handling response data with large percentages of X’s. The first uses X-canceling MISR architecture that is based on deterministically observing scan cells, and the second is a hybrid approach that combines a simple X-masking scheme with the X-canceling MISR for further gains in test compression. The third and fourth techniques revolve around reiterative LFSR X-masking, which take advantage of LFSR-encoded masks that can be reused for multiple scan slices in novel ways.Electrical and Computer Engineerin

Surge-varying LOS based path following of under actuated surface vehicles

1048-1055Subject to harsh ocean environment, a novel path following control scheme with accurate guidance and high anti-disturbance ability for under actuated surface vehicles is proposed. The innovative work is as follow: (1) Based on the traditional line-of-sight (LOS), a surge-varying LOS (SVLOS) guidance law is designed to achieve double guidance of speed and heading, which enhances the flexibility and precision of the previous LOS; (2) Unknown disturbances are exactly estimated by an exact disturbance observer (EDO), wherein the limitations of bounded and asymptotic observations can be avoided; (3) The EDO-based robust tracking controllers enable accurate disturbance compensation and guided signal tracking in harsh ocean environment. Rigorous theoretical analysis and significant simulation comparison have been done to demonstrate superiority of the EDO-SVLOS scheme

Modeling and Analysis of Multiple Engine Aircraft Configurations for Fault Tolerant Control

A formal framework is presented that allows for the analysis of the potential for using engine thrust control for aircraft actuator failure accommodation. Three sets of parameters have been identified as critical: number of engines and their position, engine thrust and throttle dynamics, and type and severity of the actuator failure. A mathematical model was developed that allows for the determination of the values of some of the parameters when the others are imposed such as determining the thrust control authority when the engine locations and Euler angles are known. Additionally, the engine locations can be determined when the thrust control authority and engine Euler angles are known and the engine Euler angles can be determined when the engine locations and thrust control authority are known. A MATLAB/Simulink simulation environment was built around a model of a large transport that can accommodate up to ten engines at different locations. A fuzzy logic controller was designed and employed for failure accommodation. The fuzzy logic controller utilizes the pilot lateral, longitudinal, and directional commands as well as the aircraft\u27s pitch attitude, roll attitude, yaw attitude and respective angular rates as the inputs to the system and provides throttle commands for each engine based on its location with respect to the aircraft\u27s center of mass. Failures of varying severity on the rudder, left or right aileron, and left or right elevator were implemented. The controller was capable of accommodating an extremely severe aileron failure and moderately severe rudder failure without additional pilot input. The controller was capable of mitigating some of the pilot command required for a moderate elevator failure. The simulation environment was used to verify the analytical results and to demonstrate the fault tolerant capabilities of multiple engine configurations. It proved to be a flexible and efficient tool for analysis and control system development

QBF with Soft Variables

QBF formulae are usually considered in prenex form, i.e. the quantifierblock is completely separated from the propositional part of the QBF.Among others, the semantics of the QBF is defined by the sequence ofthe variables within the prefix, where existentially quantifiedvariables depend on all universally quantified variables stated to theleft.In this paper we extend that classical definition and consider a newquantification type which we call soft variable. The idea is toallow a flexible position and quantifier type for these variables.Hence the type of quantifier of the soft variable can also bealtered. Based on this concept, we present an optimization problemseeking an optimal prefix as defined by user-given preferences. We statean algorithm based on MaxQBF, and present several applications – mainlyfrom verification area – which can be naturally translated into theoptimization problem for QBF with soft variables. We further implementeda prototype solver for this formalism, and compare our approach toprevious work, that differently from ours does not guarantee optimalityand completeness

Comparing Maintenance Strategies for Overlays

In this paper, we present an analytical tool for understanding the performance of structured overlay networks under churn based on the master-equation approach of physics. We motivate and derive an equation for the average number of hops taken by lookups during churn, for the Chord network. We analyse this equation in detail to understand the behaviour with and without churn. We then use this understanding to predict how lookups will scale for varying peer population as well as varying the sizes of the routing tables. We then consider a change in the maintenance algorithm of the overlay, from periodic stabilisation to a reactive one which corrects fingers only when a change is detected. We generalise our earlier analysis to underdstand how the reactive strategy compares with the periodic one.Comment: 10 pages, 8 figure