Identification of Reverse Engineering Candidates utilizing Machine Learning and Aircraft Cannibalization Data

As military aircraft continue to remain in service and age, cannibalization of parts is increasing. Proactive identification of parts that are at high risk for cannibalization will inform engineering processes such as reverse engineering, thus allowing potentially reducing lead time to develop new parts. The research objective was to develop a causal structure that can be used for prediction of when cannibalization actions may occur. Bayesian networks allow encoding of causality between various descriptive features given a data set. The method utilized a tabu search algorithm, identified the underlying causal structure and the associated node probabilities. The method is then applied to an aircraft case study. The analysis resulted in a predictive algorithm with a true positive rate of 73 – 96 percent depending on the target feature. The results indicate high precision and recall for all target features. Additional research is needed in order to validate the causal structure with military personal, incorporate domain expertise, and reduce the high false alarm rate

A Critical Analysis of the Rose-Weaver Measurement Technique for BSF Silicon Solar Cells

The design of the optimal efficiency silicon solar cell requires the minimization of several performance-limiting phenomena. In particular, much improvement is necessary to decrease the loss of minority carriers to recombination. Thus, in order to focus our design efforts in the development of an optimal efficiency solar cell it is imperative that we be able to experimentally distinguish and assign values to the different forms of minority carrier recombination in the cell. Recently, B. H. Rose and H. T. Weaver of Sandia National Laboratories have proposed a method to evaluate minority carrier recombination in the back surface field (BSF) solar cell through measurement of the short circuit current and open circuit voltage decay fates. In this thesis, we critically analyze the Rose-Weaver Method. In particular, we investigate the mathematical model formulated by Rose and Weaver to describe the decay of the short circuit current and the open circuit voltage. A study of the model equations reveals that, for even small fluctuations in the experimental measurements, a large variation in the model solutions occurs. Moreover, the solution of the model is shown to rely on extremely accurate (perhaps unobtainably accurate) knowledge of material parameters. To avoid dependence on material parameters, we analyze a second experiment in which the back surface field of the solar cell is removed. However, the model which results from combining this experiment with the original experiment shows an even greater variation of the model solutions to uncertainty in the experimental measurements. In summary, unless we make extremely precise measurements and avoid dependence on imprecisely known material parameters, particularly, n;, the Rose-Weaver Method can lead to radically different descriptions of minority carrier recombination in the solar cell

Exploring Professional Identity

This thesis examines the ways in which organizational members define what it means to communicate professionally, the extent to which enacting professional identity reflects organizational identification and individual identity, and the specific contexts in which professionalism is most encouraged. Data collected from participant interviews highlighted three intersectional components related to the enactment professional identity and perceptions of [un]professionalism—technical, behavioral, and social. Further, the results of this study suggest that professionalism as a communicative construct manifests itself in the midst of ongoing tension between individual agency and organizational constraint, conflating individual identities with norms, values, and expectations set forth by the organization in relation to the external environment. While each of these components can be considered separately in terms of their unique properties and dimensions, it is in their intersections that the most salient symbolic and material consequences for professional identities are manifested. Through analyzing the ways in which participants do professional identity in light of situated norms, this research offers a new model of professionalism that recognizes the intersectional relationship among individuals, organizations, and the overarching environment. Future work should investigate the construct of professional identity in nontraditional organizational settings, as well as how professionalism operates in relation to dominant discourses of identity (i.e., race, class, gender, sexuality, age, ability, etc.)

Bayesian Network Analysis for Diagnostics and Prognostics of Engineering Systems

Bayesian networks have been applied to many different domains to perform prognostics, reduce risk and ultimately improve decision making. However, these methods have not been applied to military field and human performance data sets in an industrial environment. Methods frequently rely on a clear understanding of causal connections leading to an undesirable event and detailed understanding of the system behavior. Methods may also require large amount of analyst teams and domain experts, coupled with manual data cleansing and classification. The research performed utilized machine learning algorithms (such as Bayesian networks) and two existing data sets. The primary objective of the research was to develop a diagnostic and prognostic tool utilizing Bayesian networks that does not require the need for detailed causal understanding of the underlying system. The research yielded a predictive method with substantial benefits over reactive methods. The research indicated Bayesian networks can be trained and utilized to predict failure of several important components to include potential malfunction codes and downtime on a real-world Navy data set. The research also considered potential error within the training data set. The results provided credence to utilization of Bayesian networks in real field data – which will always contain error that is not easily quantified. Research should be replicated with additional field data sets from other aircraft. Future research should be conducted to solicit and incorporate domain expertise into subsequent models. Research should also consider incorporation of text based analytics for text fields, which was considered out of scope for this research project

Investigation of Human Subjectivity during Failure Mode Effects Analysis (FMEA)

Several concerns with Failure Modes and Effects Analysis (FMEA), including acknowledgement that the process contains human subjectivity, can be found in literature; however very little research has been conducted to identify where and to what extent this variation is found. This thesis investigated sources of variation related to human decision making within FMEA. Participants were required to determine the effects of given failure modes by selection of a severity level given varied input information. The study found that participants were not able to sift through the provided information and identify the appropriate cues relating data relevance to the failure mode under analysis. Thus, it appeared that more information will reduce conservatism – however the quality of the information and experience level does not have an effect. The study concluded that FMEAs contain significant subjectivity and data quality assessment must form part of the FMEA framework

Investigation of Human Subjectivity during Failure Mode Effects Analysis (FMEA)

Several concerns with Failure Modes and Effects Analysis (FMEA), including acknowledgement that the process contains human subjectivity, can be found in literature; however very little research has been conducted to identify where and to what extent this variation is found. This thesis investigated sources of variation related to human decision making within FMEA. Participants were required to determine the effects of given failure modes by selection of a severity level given varied input information. The study found that participants were not able to sift through the provided information and identify the appropriate cues relating data relevance to the failure mode under analysis. Thus, it appeared that more information will reduce conservatism – however the quality of the information and experience level does not have an effect. The study concluded that FMEAs contain significant subjectivity and data quality assessment must form part of the FMEA framework

Unclaimed Children Revisited: Focusing on Outcomes—A Case Study of the Michigan Level of Functioning Project

The major aim of Unclaimed Children Revisited is to identify policy-supported state efforts to promote quality of care for children and youth with mental health conditions in the public mental health system. The Michigan Case Study examines one such initiative - Michigan’s Level of Functioning Project (LOF). It is a 14 year-old effort to monitor and improve outcomes for children and youth with severe emotional disturbance (SED), through the use of the Child and Adolescent Functional Assessment Scale (CAFAS), a research-based tool that measures children’s daily functioning. The LOF aims to gather data on outcomes for children and youth with SED served through Michigan’s public mental health system. This data can then be used to inform decision-making at the policy and service delivery levels