The success and effectiveness of modern lean manufacturing concepts requires robust and highly reliable machinery. In this thesis, we develop several simulation studies to compare the performance of a several manufacturing systems under different maintenance polices. The main focus of this work is to compare traditional time-based maintenance policies with degradation-based predictive maintenance policies that utilize real-time sensory information to assist in decisions regarding maintenance management and component replacement. The simulation studies developed in this thesis demonstrate the benefits of using sensor-based degradation models to predict failure.

It is widely known that young drivers are overrepresented in the crash data for a variety of reasons such as risk perception and acceptance, age, gender, experience, exposure, and social contexts. The current mitigations implemented to address the issue consist mainly of graduated driver's licenses and parental involvement programs. However, as technology begins to find its way into transportation in the form of advanced driver assistance systems, there is a need to understand whether these technologies will be a benefit or a detriment to young novice drivers. The present study investigates the reaction of young novice drivers to a control intervention lane departure warning. The results showed less urgent reactions to the warning from novice drivers as compared to their more experienced counterparts. However, no differences in perceptions of the system were found between the novice and experienced groups. Nonetheless, young novice males were found to have derogated performance compared to their novice female peers as well as older more experienced male drivers. This study is a small stepping stone in the necessary investigations of effects of advanced driver assistance systems on young novice drivers and the associated young driver safety epidemic.

In quality control applications, the most basic tasks are monitoring and fault diagnosis. Monitoring results determines if diagnosis is required, and conversely, diagnostic results aids better monitoring design. Quality monitoring and fault diagnosis are closely related but also have significant difference. Essentially. monitoring focus on online changepoint detection, whilst the primary objective of diagnosis is to identify fault root causes as an offline method. Several critical problems arise in the research of quality control: firstly, whether process monitoring is able to distinguish systematic or assignable faults and occasional deviation; secondly, how to diagnose faults with coupled root causes in complex manufacturing systems; thirdly, if the changepoint and root causes of faults can be diagnosed simultaneously. In Chapter 2, we propose a novel Bayesian statistical process control method for count data in the presence of outliers. That is, we discuss how to discern out of control status and temporary abnormal process behaviors in practice, which is incapable for current SPC methodologies. In this work, process states are modeled as latent variables and inferred by the sequential Monte Carlo method. The idea of Rao-Blackwellization is employed in the approach to control detection error and computational cost. Another contribution of this work is that our method possesses self-starting characteristics, which makes the method a more robust SPC tool for discrete data. Sensitivity analysis on monitoring parameter settings is also implemented to provide practical guidelines. In Chapter 3, we study the diagnosis of dimensional faults in manufacturing. A novel Bayesian variable selection oriented diagnostic framework is proposed. Dimensional fault sources are not explicitly measurable; instead, they are connected with dimensional measurements by a generalized linear mixed effect model, based on which we further construct a hierarchical quality-fault model to conduct Bayesian inference. A reversible jump Markov Chain Monte Carlo algorithm is developed to estimate the approximate posterior probability of fault patterns. Such diagnostic procedure is superior over previous studies since no numeric regularization is required for decision making. The proposed Bayesian diagnosis can further lean towards sparse fault patterns by choosing suitable priors, in order to handle the challenge from the diagnosability of faults. Our work considers the diagnosability in building dimensional diagnostic methodologies. We explain that the diagnostic result is trustworthy for most manufacturing systems in practice. The convergence analysis is also implemented, considering the trans-dimensional nature of the diagnostic method. In Chapter 4 of the thesis, we consider the diagnosis of multivariate linear profile models. We assume liner profiles as piece-wise constant. We propose an integrated Bayesian diagnostic method to answer two problems: firstly, whether and when the process is shifted, and secondly, in which pattern the shift occurs. The method can be applied for both Phase I and Phase II needs. For Phase I diagnosis, the method is implemented with no knowledge of in control profiles, whereas in Phase II diagnosis, the method only requires partial observations. To identify exactly which profile components deviate from nominal value, the variability of the value of profile components is marginalized out through a fully Bayesian approach. To address computational difficulty, we implement Monte Carlo Method to alternatively inspect between spaces of changepoint positions and fault patterns. The diagnostic method is capable to be applied under multiple scenarios.

Source: Dissertation Abstracts International, Volume: 34-04, Section: A, page: 1670. / Thesis (Educat.D.)--The Florida State University, 1973.

No description available.

No description available.

Thesis (Ph. D.)--University of Hong Kong, 2007. / Title proper from title frame. Also available in printed format.

In an attempt to improve the process of supplying coal by way of water to Progress Energyâs Crystal River power plant, a simulation-based technique was developed to find the best schedule of coal barges. The technique uses discrete event simulation principles to find the best solution based on two criteria: minimal demurrage cost and maximal coal tons moved. Many factors are taken into account including channel capacity, tide dependencies, weather delays, periods of scheduled down time, and percentage of trips to each coal terminal. The same technique is also used for long range planning in the decisions of capital allocation of equipment, barge contracts, and coal supplier contracts. A Graphical User Interface coupled with Visual Basic .Net (VB .Net) code is used to implement the approach in a user-friendly and maintainable environment.

It is a well established fact that design undergoes stages from imprecision to precision. In the early design stages, fuzzy logic is a natural tool for modeling since it is by definition an imprecise representation. The mathematics behind fuzzy numbers have been well developed and defined in literature; yet, very little research exists in the form of fuzzy trigonometric functions. Two design problems are presented to support the motivation behind this research followed by a review of fuzzy set theory. Several approaches for mapping Y = cos(X) into the fuzzy realm are then discussed followed by the development of special purpose fuzzy trigonometric functions and fuzzy inverse trigonometric functions which are computationally simple and easy to implement. With these functions, 8 forward and 6 inverse trigonometric identities are shown to exist in the fuzzy realm. The proposal concludes by examining three engineering problems. The first problem involves the design of a fuzzy truss bridge with fuzzy forces. The second problem analyzes fuzzy forces on a block positioned on an inclined plane. The last example utilizes the fuzzy inverse trigonometric functions to calculate fuzzy bond angles within a chemical compound.

Environmental regulations or the necessity for a green image due to growing environmental concerns as well as the potential economical benefits of product recovery have pushed manufacturers to integrate product recovery management with their manufacturing process. Consequently, production planning and inventory control of recoverable manufacturing systems has gained significant interest among researchers who aim to contribute to industrial practice. This dissertation considers inventory optimization of a single product recoverable manufacturing system where stochastic demand is met by either newly manufactured items or remanufactured items. Lead times and set up costs for manufacturing and remanufacturing are considered. The inventory optimization problem for this system is formulated as a Markov decision process (MDP) and through an empirical study, optimal or near-optimal policy characterizations under several cost configurations and several lead time cases for manufacturing and remanufacturing are determined. The effects of a change in cost parameters of the system on the optimal policy structure as well as policy parameter values are investigated. Results indicate that the existence of set up cost for either manufacturing or remanufacturing has a significant effect on policy structure. Consequently, an MDP-based search procedure is introduced to determine the inventory policy characterizations given that appropriate policy structures under certain cost configurations are known. Further, a neural network analysis is performed to determine the functional relationships between cost parameters of the system and the inventory policy parameter values. Results indicate that the policy characterizations found by either MDP-based search methodology or the formulae provided by neural network are optimal or near-optimal with small deviations (usually, less than 1%) from optimal cost. Finally, the optimal inventory policies are investigated through the entire product life cycle of a remanufacturable product. Benefiting from the MDP analysis, the optimal or near-optimal policy characterizations with only a few parameters are determined for every stage of the product life cycle. The effects of a change in the demand and return rates on the optimal inventory policies are investigated. Further, the performance of these long-run policy characterizations is evaluated in a finite-horizon setting, and the importance of frequently revising the inventory policies over the product life cycle is illustrated numerically.