Semantic technologies for supporting KDD processes

209 p.Achieving a comfortable thermal situation within buildings with an efficient use of energy remains still an open challenge for most buildings. In this regard, IoT (Internet of Things) and KDD (Knowledge Discovery in Databases) processes may be combined to solve these problems, even though data analysts may feel overwhelmed by heterogeneity and volume of the data to be considered. Data analysts could benefit from an application assistant that supports them throughout the KDD process. This research work aims at supporting data analysts through the different KDD phases towards the achievement of energy efficiency and thermal comfort in tertiary buildings. To do so, the EEPSA (Energy Efficiency Prediction Semantic Assistant) is proposed, which aids data analysts discovering the most relevant variables for the matter at hand, and informs them about relationships among relevant data. This assistant leverages Semantic Technologies such as ontologies, ontology-driven rules and ontology-driven data access. More specifically, the EEPSA ontology is the cornerstone of the assistant. This ontology is developed on top of three ODPs (Ontology Design Patterns) and it is designed so that its customization to address similar problems in different types of buildings can be approached methodically

A Statistical Evaluation of Risk Priority Numbers in Failure Modes and Effects Analysis Applied to the Prediction of Complex Systems

Complex systems such as military aircraft and naval ships are difficult to cost effectively maintain. Frequently, large-scale maintenance of complex systems (i.e., a naval vessel) is based on the reduction of the system to its base subcomponents and the use of manufacturer-suggested, time-directed, preventative maintenance, which is augmented during the systems lifecycle with predictive maintenance which assesses the system\u27s ability to perform its mission objectives. While preventative maintenance under certain conditions can increase reliability, preventative maintenance systems are often costly, increase down time, and allow for maintenance-induced failures, which may decrease the reliability of the system (Ebeling, 1997). This maintenance scheme ignores the complexity of the system it tries to maintain. By combining the base components or subsystems into a larger system, and introducing human interaction with the system, the complexity of the system creates a unique entity that cannot be completely understood by basing predictability of the system to perform tasks on the reduction of the system to its subcomponents. This study adds to the scholarly literature by developing a model, based on the traditional failure modes and effects analysis commonly used for research and development projects, to capture the effects of the human interaction with the system. Based on the ability of personnel assigned to operate and maintain the system, the severity of the system failure on the impact on the metasystems ability to perform its mission and the likelihood of the event of the failure to occur. Findings of the research indicate that the human interaction with the system, in as far as the ability of the personnel to repair and maintain the system, is a vital component in the ability to predict likelihood of the system failure and the prioritization of the risk of system failure, may be adequately captured for analysis through use of expert opinion elicitation. The use of the expert\u27s opinions may provide additional robustness to the modeling and analysis of system behavior in the event that failure occurs

Model Based Mission Assurance in a Model Based Systems Engineering (MBSE) Framework: State-of-the-Art Assessment

This report explores the current state of the art of Safety and Mission Assurance (S&MA) in projects that have shifted towards Model Based Systems Engineering (MBSE). Its goal is to provide insight into how NASA's Office of Safety and Mission Assurance (OSMA) should respond to this shift. In MBSE, systems engineering information is organized and represented in models: rigorous computer-based representations, which collectively make many activities easier to perform, less error prone, and scalable. S&MA practices must shift accordingly. The "Objective Structure Hierarchies" recently developed by OSMA provide the framework for understanding this shift. Although the objectives themselves will remain constant, S&MA practices (activities, processes, tools) to achieve them are subject to change. This report presents insights derived from literature studies and interviews. The literature studies gleaned assurance implications from reports of space-related applications of MBSE. The interviews with knowledgeable S&MA and MBSE personnel discovered concerns and ideas for how assurance may adapt. Preliminary findings and observations are presented on the state of practice of S&MA with respect to MBSE, how it is already changing, and how it is likely to change further. Finally, recommendations are provided on how to foster the evolution of S&MA to best fit with MBSE

Knowledge Reuse Through Electronic Knowledge Repositories: An Empirical Study And Ontological Improvement Effort For The Manufacturing Industry

Knowledge management adoption is growing, and will continue to grow in no small part because of its recent inclusion into the ISO 9001 quality standard. As organizations look towards ways in which to manage their knowledge, the codification of explicit knowledge through Knowledge Management Systems (KMS) and Electronic Knowledge Repositories (EKRs) will undoubtedly gain more interest. An EKR is a form of KMS that emphasizes the codification and storage of organizational expertise for the purposes of Knowledge Reuse (KRU). Unfortunately, the factors surrounding KRU are not well understood. While previous studies have viewed EKR usage from a narrow perspective, a broader and interconnected view of KRU via EKRs has yet to emerge. Additionally, while there have been numerous benefits linked to EKRs, there are still issues that limit their utility, particularly in the manufacturing arena where information complexity and geography have made it increasingly difficult to share knowledge. Hence, this research employed a two pronged approach. First, using a multi-theoretical perspective to model KRU via EKRs, a quantitative study was conducted and identified several socio-technical factors that predicted greater KRU. These factors had not been previously modeled within the context of KRU via EKRs, and hence add to both the theoretical and practical implications of the domain. Additionally, the KRU construct was also tied to a back end resulting outcome view that was informed by the Expectation Confirmation Model (ECM). Through this view, the research quantitatively validated that KRU not only predicted greater performance, but also impacted greater knowledge sharing and continuance of use. This ancillary benefit helps to reinforce the importance of EKRs in that additional gains are manifested along with the core component of KRU. Second, the research extended the capability of manufacturing EKRs by developing a holistic design and process based ontology that connects key concepts within these domains to provide an overall interconnected view. Additionally, to ensure the relevance of the ontology, a mature and globally recognized industry standard was used as the basis to develop it. The ontology was then formalized and tested via Semantic Web tools: Protege, RDF, and SPARQL. The results demonstrate an improved approach to knowledge recall by providing rich and accurate query returns. The ability to use standalone and federated queries to effectively cull the complexity of this interconnected domain is an enhancement to keyword based and traditional relational database approaches. Additionally, to assist with greater industry adoption a systematic and constructive approach for developing and operationalizing the ontology is provided. Finally, in the spirit of the program in which this dissertation is presented, rounding out the research effort are broader organizational management recommendations for overall knowledge management. Referencing industry targeted literature and syncing them with findings from these two research efforts, several pragmatic and sequentially logical approaches to knowledge management are offered

Integrated Frameworks for Effective Multi-criteria Decision Making in Reliability Centred Maintenance of Industrial Machines

No abstract availabl

Diagnóstico de fallos y optimización de la planificación en un marco de e-mantenimiento.

324 p.El objetivo principal es demostrar el potencial de mejora que las técnicas y metodologías relacionadas con la analítica prescriptiva, pueden proporcionar en aplicaciones de mantenimiento industrial. Las tecnologías desarrolladas se pueden agrupar en tres ámbitos: - El e-mantenimiento, relacionado fundamentalmente con el desarrollo de plataformas colaborativas e inteligentes que permiten la integración de nuevos sensores, sistemas de comunicaciones, estándares y protocolos, conceptos, métodos de almacenamiento y análisis etc. que entran continuamente en nuestro abanico de posibilidades y nos ofrecen la posibilidad de seguir una tendencia de mejora en la optimización de activos y procesos, y en la interoperabilidad entre sistemas.- Las Redes Bayesianas (Bayesian Networks ¿ BNs) junto con otras metodologías de recogida de información utilizadas en ingeniería nos ofrecen la posibilidad de automatizar la tarea de diagnóstico y predicción de fallos.- La optimización de las estrategias de mantenimiento, mediante simulaciones de fallos y análisis coste-efectividad, que ayudan a la toma de decisiones a la hora de seleccionar una estrategia de mantenimiento adecuada para el activo. Además, mediante el uso de algoritmos de optimización logramos mejorar la planificación del mantenimiento, reduciendo los tiempos y costes para realizar las tareas en un parque de activos

Assurance of Machine Learning-Based Aerospace Systems: Towards an Overarching Properties-Driven Approach

692M15-22-T-00012Traditional process-based approaches of certifying aerospace digital systems are not sufficient to address the challenges associated with using Artificial Intelligence (AI) or Machine Learning (ML) techniques. To address this, agencies are evaluating an alternative Means of Compliance (MoC) called the Overarching Properties (OP). The goals for this research are to develop recommendations and assurance criteria and to explore safety risk mitigation approaches for such AI/ML-based software systems. This document outlines a novel foundation for the application of OPs to support the assurance and certification of complex aerospace digital systems consisting of AI/ML-based components. To this end, we first select the use case of a Recorder Independent Power Supply (RIPS) system. We then perform a Functional Hazard Assessment (FHA) to identify a set of hazards associated with the RIPS and design a set of appropriate requirements to mitigate those hazards

Process Productivity Improvements through Semantic and Linked Data Technologies

Programa de Doctorado en Ciencia y Tecnología Informática por la Universidad Carlos III de MadridPresidente: José María Álvarez Rodríguez.- Secretario: Rafael Valencia García.- Vocal: Alejandro Rodríguez Gonzále

Cyber-security Risk Assessment

Cyber-security domain is inherently dynamic. Not only does system configuration changes frequently (with new releases and patches), but also new attacks and vulnerabilities are regularly discovered. The threat in cyber-security is human, and hence intelligent in nature. The attacker adapts to the situation, target environment, and countermeasures. Attack actions are also driven by attacker's exploratory nature, thought process, motivation, strategy, and preferences. Current security risk assessment is driven by cyber-security expert's theories about this attacker behavior. The goal of this dissertation is to automatically generate the cyber-security risk scenarios by: * Capturing diverse and dispersed cyber-security knowledge * Assuming that there are unknowns in the cyber-security domain, and new knowledge is available frequently * Emulating the attacker's exploratory nature, thought process, motivation, strategy, preferences and his/her interaction with the target environment * Using the cyber-security expert's theories about attacker behavior The proposed framework is designed by using the unique cyber-security domain requirements identified in this dissertation and by overcoming the limitations of current risk scenario generation frameworks. The proposed framework automates the risk scenario generation by using the knowledge as it becomes available (or changes). It supports observing, encoding, validating, and calibrating cyber-security expert's theories. It can also be used for assisting the red-teaming process. The proposed framework generates ranked attack trees and encodes the attacker behavior theories. These can be used for prioritizing vulnerability remediation. The proposed framework is currently being extended for developing an automated threat response framework that can be used to analyze and recommend countermeasures. This framework contains behavior driven countermeasures that uses the attacker behavior theories to lead the attacker away from the system to be protected