We propose a mathematical framework that integrates low-level sensory signals from monitoring engineering systems and their components with high-level decision models for maintenance optimization. Our objective is to derive optimal adaptive maintenance strategies that capitalize on condition monitoring information to update maintenance actions based upon the current state of health of the system. We refer to this sensor-based decision methodology as "sense-and-respond logistics". As a first step, we develop and extend degradation models to compute and periodically update the remaining life distribution of fielded components using in situ degradation signals. Next, we integrate these sensory updated remaining life distributions with maintenance decision models to; (1) determine, in real-time, the optimal time to replace a component such that the lost opportunity costs due to early replacements are minimized and system utilization is increased, and (2) sequentially determine the optimal time to order a spare part such that inventory holding costs are minimized while preventing stock outs. Lastly, we integrate the proposed degradation model with Markov process models to derive structured replacement and spare parts ordering policies. In particular, we show that the optimal maintenance policy for our problem setting is a monotonically non-decreasing control limit type policy. We validate our methodology using real-world data from monitoring a piece of rotating machinery using vibration accelerometers. We also demonstrate that the proposed sense-and-respond decision methodology results in better decisions and reduced costs compared to other traditional approaches.

In this thesis, several Reliability-based Design Optimization (RBDO) methods and algorithms for airplane floor grid layout optimization are proposed. A general RBDO process is proposed and validated by an example. Copula as a mathematical method to model random variable correlations is introduced to discover the correlations between random variables and to be applied in producing correlated data samples for Monte Carlo simulations. Based on Hasofer-Lind (HL) method, a correlated HL method is proposed to evaluate a reliability index under correlation. As an alternative method for computing a reliability index, the reliability index is interpreted as an optimization problem and two nonlinear programming algorithms are introduced to evaluate reliability index. To evaluate the reliability index by Monte Carlo simulation in a time efficient way, a kriging-based surrogate model is proposed and compared to the original model in terms of computing time. Since in RBDO optimization models the reliability constraint obtained by MCS does not have an analytical form, a kriging-based response surface is built. Kriging-based response surface models are usually segment functions that do not have a uniform expression over the design space; however, most optimization algorithms require a uniform expression for constraints. To solve this problem, a heuristic gradient-based direct searching algorithm is proposed. These methods and algorithms, together with the RBDO general process, are applied to the layout optimization of aircraft floor grid structural design.

With the current increased emphasis on the development of energy optimized vehicle systems architectures during the early phases in aircraft conceptual design, accurate predictions of these off-nominal requirements are needed to justify architecture concept selection. A process was developed for capturing architecture specific performance degradation strategies and optimally imposing their associated requirements. This process is enabled by analog extensions to traditional safety design and assessment tools and consists of six phases: Continuous Functional Hazard Assessment, Architecture Definition, Load Shedding Optimization, Analog System Safety Assessment, Architecture Optimization, and Architecture Augmentation. Systematic off-nominal analysis of requirements was performed for dissimilar architecture concepts. It was shown that traditional discrete application of safety and reliability requirements have adverse effects on the prediction of requirements. This design bias was illustrated by cumulative unit importance metrics. Low fidelity representations of the loss/hazard relationship place undue importance on some units and yield under or over-predictions of system performance.

The development and implementation challenges associated with a proposed load allocation paradigm for fault risk assessment and system health management based on uncertain fault diagnostic and failure prognostic information are investigated. Health management actions are formulated in terms of a value associated with improving system reliability, and a cost associated with inducing deviations from a system's nominal performance. Three simulated case study systems are considered to highlight some of the fundamental challenges of formulating and solving an optimization on the space of available supervisory control actions in the described health management architecture. Repeated simulation studies on the three case-study systems are used to illustrate an empirical approach for tuning the conservatism of health management policies by way of adjusting risk assessment metrics in the proposed health management paradigm. The implementation and testing of a real-world prognostic system is presented to illustrate model development challenges not directly addressed in the analysis of the simulated case study systems. Real-time battery charge depletion prediction for a small unmanned aerial vehicle is considered in the real-world case study. An architecture for offline testing of prognostics and decision making algorithms is explained to facilitate empirical tuning of risk assessment metrics and health management policies, as was demonstrated for the three simulated case study systems.

Electricity generated from photovoltaics (PV) promises to satisfy the world's ever-growing thirst for energy without significant pollution and greenhouse gas emissions. At present, however, PV is several times too expensive to compete economically with conventional sources of electricity delivered via the power grid. To ensure long-term success, must achieve cost parity with electricity generated by conventional sources of electricity. This requires detailed understanding of the relationship between technology and economics as it pertains to PV devices and systems. The research tasks of this thesis focus on developing and using four types of models in concert to develop a complete picture of how solar cell technology and design choices affect the quantity and cost of energy produced by PV systems. It is shown in this thesis that high-efficiency solar cells can leverage balance-of-systems (BOS) costs to gain an economic advantage over solar cells with low efficiencies. This advantage is quantified and dubbed the "efficiency premium." Solar cell device models are linked to models of manufacturing cost and PV system performance to estimate both PV system cost and performance. These, in turn, are linked to a model of levelized electricity cost to estimate the per-kilowatt-hour cost of electricity produced by the PV system. A numerical PV module manufacturing cost model is developed to facilitate this analysis. The models and methods developed in this thesis are used to propose a roadmap to high-efficiency multicrystalline silicon PV modules that achieve cost parity with electricity from the grid. The impact of PV system failures on the cost of electricity is also investigated; from this, a methodology is proposed for improving the reliability of PV inverters.

The objective of the proposed research is to develop a cost-effective power flow controller to improve the utilization and reliability of the existing transmission, sub-transmission, and distribution networks. Over the last two decades, electricity consumption and generation have continually grown at an annual rate of around 2.5%. At the same time, investments in the Transmission and Distribution (T&D) infrastructure have steadily declined. Further, it has become increasingly difficult and expensive to build new power lines. As a result, the aging power-grid has become congested and is under stress, resulting in compromised reliability and higher energy costs. In such an environment it becomes important that existing assets are used effectively to achieve highest efficiency. System reliability is sacrosanct and cannot be compromised. Utility system planners are moving from radial towards networked systems to achieve higher reliability, especially under contingency conditions. While enhancing reliability, this has degraded the controllability of the network, as current flow along individual lines can no longer be controlled. The transfer capacity of the system gets limited by the first line that reaches the thermal capacity, even when majority of the lines are operating at a fraction of their capacity. The utilization of the system gets further degraded as the lines are operated with spare capacity to sustain overloads under contingencies. Market efficiency is also sub-optimal, with congestion on key corridors restricting the low-cost generators to connect to the end users, resulting in higher electricity prices for the consumers. The proposed technology offers the capability to realize a controllable meshed-network, with the ability to utilize static and dynamic capacity of the grid to provide system-wide benefits, including- increased line and system-capacity utilization, increased reliability, improved operation under contingencies, and rapid implementation. It would allow a broadening of the energy market, permitting owners to direct how energy flows on their wires, and making it easier to connect to new sources of generation.

International Atomic Energy Agency (IAEA) safeguards are one method of proliferation resistance which is applied at most nuclear facilities worldwide. IAEA safeguards act to prevent the diversion of nuclear materials from a facility through the deterrence of detection. However, even with IAEA safeguards present at a facility, the country where the facility is located may still attempt to proliferate nuclear material by exploiting weaknesses in the safeguards system. The IAEA's mission is to detect the diversion of nuclear materials as soon as possible and ideally before it can be weaponized. Modern IAEA safeguards utilize unattended monitoring systems (UMS) to perform nuclear material accountancy and maintain the continuity of knowledge with regards to the position of nuclear material at a facility. This research focuses on evaluating the reliability of unattended monitoring systems and integrating the probabilistic failure of these systems into the comprehensive probabilistic proliferation resistance model of a facility. To accomplish this, this research applies reliability engineering analysis methods to probabilistic proliferation resistance modeling. This approach is demonstrated through the analysis of a safeguards design for the Example Sodium Fast Reactor Fuel Cycle Facility (ESFR FCF). The ESFR FCF UMS were analyzed to demonstrate the analysis and design processes that an analyst or designer would go through when evaluating/designing the proliferation resistance component of a safeguards system. When comparing the mean time to failure (MTTF) for the system without redundancies versus one with redundancies, it is apparent that redundancies are necessary to achieve a design without routine failures. A reliability engineering approach to probabilistic safeguards system analysis and design can be used to reach meaningful conclusions regarding the proliferation resistance of a UMS. The methods developed in this research provide analysts and designers alike a process to follow to evaluate the reliability of a UMS.

Traditionally design engineers have used the Factor of Safety method for ensuring that designs do not fail in the field. Access to advanced computational tools and resources have made this process obsolete and new methods to introduce higher levels of reliability in an engineering systems are currently being investigated. However, even though high computational resources are available the computational resources required by reliability analysis procedures leave much to be desired. Furthermore, the regression based surrogate modeling techniques fail when there is discontinuity in the design space, caused by failure mechanisms, when the design is required to perform under severe externalities. Hence, in this research we propose efficient Semi-Supervised Learning based surrogate modeling techniques that will enable accurate estimation of a system's response, even under discontinuity. These methods combine the available set of labeled dataset and unlabeled dataset and provide better models than using labeled data alone. Labeled data is expensive to obtain since the responses have to be evaluated whereas unlabeled data is available in plenty, during reliability estimation, since the PDF information of uncertain variables is assumed to be known. This superior performance is gained by combining the efficiency of Probabilistic Neural Networks (PNN) for classification and Expectation-Maximization (EM) algorithm for treating the unlabeled data as labeled data with hidden labels.

Bibliography: p. 474-514. / xli, 601 p. (some folded) : ill., maps (one col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, 1995

Bibliography: p. 474-514. / xli, 601 p. (some folded) : ill., maps (one col.) ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, 1995