PREDICTIVE MAINTENANCE USING MACHINE LEARNING AND EXISTING DATA SOURCES

Includes supplementary materialThe United States Marine Corps must address material-readiness challenges with emerging technologies at minimum cost. Predictive maintenance using machine learning is a growing field that can be applied using free or commercial-off-the-shelf software. Naval aviation organizations already maintain a network of data repositories that collect and store current and historical data on repairable flight-critical components. Many components fail before their expected structural life as published their manufacturers, which results in costly unscheduled maintenance. The ability to predict component failures and plan for their replacement or repair can significantly increase operational readiness. This thesis develops and analyzes machine-learning models to predict the conditional probability of failure of various MV-22B flight-critical components using data from existing Naval aviation repositories. Data preprocessing, model training, and predictions use commercial-off-the-shelf software. This work can help improve material readiness and acclimatize military-aviation personnel to emerging technologies in decision making.Captain, United States Marine CorpsApproved for public release. Distribution is unlimited

COTS-Based Eligibility – Is it a Silver Bullet?

This PowerPoint presentation explains Commercial-­‐off-­‐the-­‐Shelf software as applicable to South Carolina Healthy Connections

Identification of Critical Risk Phase in Commercial-off-the-Shelf Software (CBSD) using FMEA approach

COTS based development is becoming a popular software development approach for building large organizational software using existing developed components. COTS based approach provides pre-developed components either as in house or commercial off the shelf components, which reduces effort and cost for developing the software. There are potential challenges, risks and complexities in using COTS components. This paper provides an analysis of risks and challenges faced during developing software using CBSD approach. The risks under various phases are identified, categorized and prioritized the risks in various phases of CBSD and provide the mitigation strategy to manage the risks

“Gaining Organisational Acceptance of a Data Analytics Programme in Eli Lilly”

This paper describes how Eli Lilly overcame barriers to enable new use of data. This was achieved by adopting a deep understanding of end-user needs and the support of site leaders and end-users to standardise process monitoring. A standard approach of ‘Contexting’, ‘Data-Extraction’, ‘Model build’, ‘On-line Config’, ‘Deploy’, was utilised, using a commercial off-the-shelf software. The paper describes proof of concept demonstrations undertaken and discusses the ingredients to make the journey a success

Spacecraft Internal Acoustic Environment Modeling

The objective of the project is to develop an acoustic modeling capability, based on commercial off-the-shelf software, to be used as a tool for oversight of the future manned Constellation vehicles. The use of such a model will help ensure compliance with acoustic requirements. Also, this project includes modeling validation and development feedback via building physical mockups and conducting acoustic measurements to compare with the predictions

Integrity static analysis of COTS/SOUP

This paper describes the integrity static analysis approach developed to support the justification of commercial off-the-shelf software (COTS) used in a safety-related system. The static analysis was part of an overall software qualification programme, which also included the work reported in our paper presented at Safecomp 2002. Integrity static analysis focuses on unsafe language constructs and “covert” flows, where one thread can affect the data or control flow of another thread. The analysis addressed two main aspects: the internal integrity of the code (especially for the more critical functions), and the intra-component integrity, checking for covert channels. The analysis process was supported by an aggregation of tools, combined and engineered to support the checks done and to scale as necessary. Integrity static analysis is feasible for industrial scale software, did not require unreasonable resources and we provide data that illustrates its contribution to the software qualification programme

Ground System Harmonization Efforts at NASA's Goddard Space Flight Center

This slide presentation reviews the efforts made at Goddard Space Flight Center in harmonizing the ground systems to assist in collaboration in space ventures. The key elements of this effort are: (1) Moving to a Common Framework (2) Use of Consultative Committee for Space Data Systems (CCSDS) Standards (3) Collaboration Across NASA Centers (4) Collaboration Across Industry and other Space Organizations. These efforts are working to bring into harmony the GSFC systems with CCSDS standards to allow for common software, use of Commercial Off the Shelf Software and low risk development and operations and also to work toward harmonization with other NASA center

Using quality models in software package selection

The growing importance of commercial off-the-shelf software packages requires adapting some software engineering practices, such as requirements elicitation and testing, to this emergent framework. Also, some specific new activities arise, among which selection of software packages plays a prominent role. All the methodologies that have been proposed recently for choosing software packages compare user requirements with the packages' capabilities. There are different types of requirements, such as managerial, political, and, of course, quality requirements. Quality requirements are often difficult to check. This is partly due to their nature, but there is another reason that can be mitigated, namely the lack of structured and widespread descriptions of package domains (that is, categories of software packages such as ERP systems, graphical or data structure libraries, and so on). This absence hampers the accurate description of software packages and the precise statement of quality requirements, and consequently overall package selection and confidence in the result of the process. Our methodology for building structured quality models helps solve this drawback.Peer ReviewedPostprint (published version