Reliability analysis of encapsulation methods for lead -based paint through an accelerated life testing

The adverse health effects of long-term exposure to lead are well established, with major uptake into the human body occurring mainly through oral ingestion by young children. Lead-based paint was frequently used in homes built before 1978, particularly in inner-city areas. Minority populations experience the effects of lead poisoning disproportionately. ^ Lead-based paint abatement is costly. In the United States, residents of about 400,000 homes, occupied by 900,000 young children, lack the means to correct lead-based paint hazards. The magnitude of this problem demands research on affordable methods of hazard control. One method is encapsulation, defined as any covering or coating that acts as a permanent barrier between the lead-based paint surface and the environment. ^ Two encapsulants were tested for reliability and effective life span through an accelerated lifetime experiment that applied stresses exceeding those encountered under normal use conditions. The resulting time-to-failure data were used to extrapolate the failure time under conditions of normal use. Statistical analysis and models of the test data allow forecasting of long-term reliability relative to the 20-year encapsulation requirement. Typical housing material specimens simulating walls and doors coated with lead-based paint were overstressed before encapsulation. A second, un-aged set was also tested. Specimens were monitored after the stress test with a surface chemical testing pad to identify the presence of lead breaking through the encapsulant. ^ Graphical analysis proposed by Shapiro and Meeker and the general log-linear model developed by Cox were used to obtain results. Findings for the 80% reliability time to failure varied, with close to 21 years of life under normal use conditions for encapsulant A. The application of product A on the aged gypsum and aged wood substrates yielded slightly lower times. Encapsulant B had an 80% reliable life of 19.78 years. ^ This study reveals that encapsulation technologies can offer safe and effective control of lead-based paint hazards and may be less expensive than other options. The U.S. Department of Health and Human Services and the CDC are committed to eliminating childhood lead poisoning by 2010. This ambitious target is feasible, provided there is an efficient application of innovative technology, a goal to which this study aims to contribute. ^

A reliability centered maintenance decision system for a discrete part manufacturing facility

This thesis develops and validates the framework of a specialized maintenance decision support system for a discrete part manufacturing facility. Its construction utilizes a modular approach based on the fundamental philosophy of Reliability Centered Maintenance (RCM). The proposed architecture uniquely integrates System Decomposition, System Evaluation, Failure Analysis, Logic Tree Analysis, and Maintenance Planning modules. It presents an ideal solution to the unique maintenance inadequacies of modern discrete part manufacturing systems. Well established techniques are incorporated as building blocks of the system's modules. These include Failure Mode Effect and Criticality Analysis (FMECA), Logic Tree Analysis (LTA), Theory of Constraints (TOC), and an Expert System (ES). A Maintenance Information System (MIS) performs the system's support functions. Validation was performed by field testing of the system at a Miami based manufacturing facility. Such a maintenance support system potentially reduces downtime losses and contributes to higher product quality output. Ultimately improved profitability is the final outcome. ^

VLSI testing and characterization system for micro electro-mechanical (MEM) sensors

The purpose of this investigation was to develop and implement a general purpose VLSI (Very Large Scale Integration) Test Module based on a FPGA (Field Programmable Gate Array) system to verify the mechanical behavior and performance of MEM sensors, with associated corrective capabilities; and to make use of the evolving System-C, a new open-source HDL (Hardware Description Language), for the design of the FPGA functional units. System-C is becoming widely accepted as a platform for modeling, simulating and implementing systems consisting of both hardware and software components. In this investigation, a Dual-Axis Accelerometer (ADXL202E) and a Temperature Sensor (TMP03) were used for the test module verification. Results of the test module measurement were analyzed for repeatability and reliability, and then compared to the sensor datasheet. Further study ideas were identified based on the study and results analysis. ASIC (Application Specific Integrated Circuit) design concepts were also being pursued.

An autoregressive conditional filtering process to remove intraday seasonal volatility and its application to testing the noisy rational expectations model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.

A self -testing approach for autonomic software

As researchers and practitioners move towards a vision of software systems that configure, optimize, protect, and heal themselves, they must also consider the implications of such self-management activities on software reliability. Autonomic computing (AC) describes a new generation of software systems that are characterized by dynamically adaptive self-management features. During dynamic adaptation, autonomic systems modify their own structure and/or behavior in response to environmental changes. Adaptation can result in new system configurations and capabilities, which need to be validated at runtime to prevent costly system failures. However, although the pioneers of AC recognize that validating autonomic systems is critical to the success of the paradigm, the architectural blueprint for AC does not provide a workflow or supporting design models for runtime testing. ^ This dissertation presents a novel approach for seamlessly integrating runtime testing into autonomic software. The approach introduces an implicit self-test feature into autonomic software by tailoring the existing self-management infrastructure to runtime testing. Autonomic self-testing facilitates activities such as test execution, code coverage analysis, timed test performance, and post-test evaluation. In addition, the approach is supported by automated testing tools, and a detailed design methodology. A case study that incorporates self-testing into three autonomic applications is also presented. The findings of the study reveal that autonomic self-testing provides a flexible approach for building safe, reliable autonomic software, while limiting the development and performance overhead through software reuse. ^

An Autoregressive Conditional Filtering Process to remove Intraday Seasonal Volatility and its Application to Testing the Noisy Rational Expectations Model

We develop a new autoregressive conditional process to capture both the changes and the persistency of the intraday seasonal (U-shape) pattern of volatility in essay 1. Unlike other procedures, this approach allows for the intraday volatility pattern to change over time without the filtering process injecting a spurious pattern of noise into the filtered series. We show that prior deterministic filtering procedures are special cases of the autoregressive conditional filtering process presented here. Lagrange multiplier tests prove that the stochastic seasonal variance component is statistically significant. Specification tests using the correlogram and cross-spectral analyses prove the reliability of the autoregressive conditional filtering process. In essay 2 we develop a new methodology to decompose return variance in order to examine the informativeness embedded in the return series. The variance is decomposed into the information arrival component and the noise factor component. This decomposition methodology differs from previous studies in that both the informational variance and the noise variance are time-varying. Furthermore, the covariance of the informational component and the noisy component is no longer restricted to be zero. The resultant measure of price informativeness is defined as the informational variance divided by the total variance of the returns. The noisy rational expectations model predicts that uninformed traders react to price changes more than informed traders, since uninformed traders cannot distinguish between price changes caused by information arrivals and price changes caused by noise. This hypothesis is tested in essay 3 using intraday data with the intraday seasonal volatility component removed, as based on the procedure in the first essay. The resultant seasonally adjusted variance series is decomposed into components caused by unexpected information arrivals and by noise in order to examine informativeness.