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 development of logistics management models for the Space Transportation System

A new analytic queueing approach was described which relates stockage levels, repair level decisions, and the project network schedule of prelaunch operations directly to the probability distribution of the space transportation system launch delay. Finite source population and limited repair capability were additional factors included in this logistics management model developed specifically for STS maintenance requirements. Data presently available to support logistics decisions were based on a comparability study of heavy aircraft components. A two-phase program is recommended by which NASA would implement an integrated data collection system, assemble logistics data from previous STS flights, revise extant logistics planning and resource requirement parameters using Bayes-Lin techniques, and adjust for uncertainty surrounding logistics systems performance parameters. The implementation of these recommendations can be expected to deliver more cost-effective logistics support

Metrics Pilot Project for Military Avionics Sustainment: Experimental Design and Implementation Plan

This working paper outlines the design of an experiment, employing a pilot project, for identifying and validating new metrics for managing the US Air Force military avionics sustainment system. The paper also presents a plan for implementing the pilot project. The experimental design allows for the quantitifation of the effects of the new metrics, while controlling for the effects of other factors impacting the observed outcomes. Underlying the pilot project, and the proposed experimental design, are three main hypotheses derived from earlier research: (a) currently used metrics foster local optimization rather than system-wide optimization; (b) they do not allow measures of progress towards the achievement of system-wide goals and objectives, and, hence, do not allow visibility into the impact of depot maintenance on the warfighter; and (c) they are driving the “wrong behavior,” causing suboptimal decisions governing maintenance and repair priorities and practices and, as a result, undermining the efficiency and effectiveness of the sustainment system, despite the fact that the Air Force sustainment system has a dedicated and highly skilled workforce supporting the warfighter

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

A Survey and Analysis of Aircraft Maintenance Metrics: A Balanced Scorecard Approach

Performance metrics have helped to sustain the Air Force, improve processes, and guided decisions makers through decades of challenges and change. The Air Force continues to change as it faces the challenges of an aging fleet coupled with the tightest budget constraints of modern times. The current metrics employed by the United States Air Force Aircraft Maintenance community have gone largely unchanged over decades despite a host of force altering events. The focus of this research is to evaluate current maintenance metrics and assess the utility of the Balanced Scorecard framework for use in a Maintenance Group. The researcher utilizes a mixed methodology to accomplish this evaluation, including survey research, statistical analysis, content analysis, and correlation analysis. The paper proposes a Maintenance Group Balanced Scorecard based on the analysis of survey responses from Maintenance Officers with Combat Air Forces (CAF) experience. The proposed Balanced Scorecard is comprised of existing, refined, and proposed metrics to measure each perspective category of the Balanced Scorecard, and is intended to help align maintenance metrics with organizational goals/objectives and the strategic goals of Maintenance Groups in CAF units

The Effects of Funding Gaps on Depot Maintenance Hours

The relationship between expenditures and readiness level is a topic of interest to military senior leaders, defense resource planners, and the American taxpayer alike. Senior leaders within the Air Force (AF) justify increased defense budgets by pointing to the potential adverse effects that decreased funding could have on military readiness. Resource planners within the AF are then tasked with the responsibility of ensuring that budgets are allocated most effectively to maximize the AF\u27s ability to project airpower across a variety of contingency operations. This thesis investigates the relationship between budgets and readiness by examining the relationship between depot level funding and hours of aircraft downtime spent at the depot. Funding is analyzed in terms of the magnitude that the amount of funding receives deviates from the amount of funding requested by the planner. The analysis ultimately did not find any conclusive relationship between deviations from requested depot budget levels and the number of hours of downtime spent at the depot

Forecasting Flying Hour Costs of the B-1, B-2, and B-52 Bomber Aircraft

This thesis both evaluates, and presents improvements to, the current method of forecasting flying costs of Air Force aircraft. It uses depot level repairable (DLR) and consumable (CONS) data for the Air Force\u27s bomber platforms: B-1B, B-2, and B-52H. The current forecasting method assumes a proportional relationship between costs and flying hours such that 1) when no hours are flown costs are zero, and 2) a 1% increase in flying hours will increase costs by 1%. The findings of this research indicate that applying log-linear ordinary least squares regression techniques may be an improved fit of flying cost data over the current proportional model; the actual data indicate a non-zero intercept and a less than proportional relationship between costs and flying hours. This research also found that models including factors other than flying hours as independent variables, such as sorties, lagged costs, and fiscal trends, may be more useful than models based solely on flying hours. Finally, this research found that estimating quarterly costs at the base-level may yield more accurate estimates than estimating at the monthly level, or mission design series level

Shuttle Ground Operations Efficiencies/Technologies (SGOE/T) study. Volume 2: Ground Operations evaluation

The Ground Operations Evaluation describes the breath and depth of the various study elements selected as a result of an operational analysis conducted during the early part of the study. Analysis techniques used for the evaluation are described in detail. Elements selected for further evaluation are identified; the results of the analysis documented; and a follow-on course of action recommended. The background and rationale for developing recommendations for the current Shuttle or for future programs is presented