An ontology framework for developing platform-independent knowledge-based engineering systems in the aerospace industry

This paper presents the development of a novel knowledge-based engineering (KBE) framework for implementing platform-independent knowledge-enabled product design systems within the aerospace industry. The aim of the KBE framework is to strengthen the structure, reuse and portability of knowledge consumed within KBE systems in view of supporting the cost-effective and long-term preservation of knowledge within such systems. The proposed KBE framework uses an ontology-based approach for semantic knowledge management and adopts a model-driven architecture style from the software engineering discipline. Its phases are mainly (1) Capture knowledge required for KBE system; (2) Ontology model construct of KBE system; (3) Platform-independent model (PIM) technology selection and implementation and (4) Integration of PIM KBE knowledge with computer-aided design system. A rigorous methodology is employed which is comprised of five qualitative phases namely, requirement analysis for the KBE framework, identifying software and ontological engineering elements, integration of both elements, proof of concept prototype demonstrator and finally experts validation. A case study investigating four primitive three-dimensional geometry shapes is used to quantify the applicability of the KBE framework in the aerospace industry. Additionally, experts within the aerospace and software engineering sector validated the strengths/benefits and limitations of the KBE framework. The major benefits of the developed approach are in the reduction of man-hours required for developing KBE systems within the aerospace industry and the maintainability and abstraction of the knowledge required for developing KBE systems. This approach strengthens knowledge reuse and eliminates platform-specific approaches to developing KBE systems ensuring the preservation of KBE knowledge for the long term

Data integration support for offshore decommissioning waste management

Offshore oil and gas platforms have a design life of about 25 years whereas the techniques and tools used for managing their data are constantly evolving. Therefore, data captured about platforms during their lifetimes will be in varying forms. Additionally, due to the many stakeholders involved with a facility over its life cycle, information representation of its components varies. These challenges make data integration difficult. Over the years, data integration technology application in the oil and gas industry has focused on meeting the needs of asset life cycle stages other than decommissioning. This is the case because most assets are just reaching the end of their design lives. Currently, limited work has been done on integrating life cycle data for offshore decommissioning purposes, and reports by industry stakeholders underscore this need. This thesis proposes a method for the integration of the common data types relevant in oil and gas decommissioning. The key features of the method are that it (i) ensures semantic homogeneity using knowledge representation languages (Semantic Web) and domain specific reference data (ISO 15926); and (ii) allows stakeholders to continue to use their current applications. Prototypes of the framework have been implemented using open source software applications and performance measures made. The work of this thesis has been motivated by the business case of reusing offshore decommissioning waste items. The framework developed is generic and can be applied whenever there is a need to integrate and query disparate data involving oil and gas assets. The prototypes presented show how the data management challenges associated with assessing the suitability of decommissioned offshore facility items for reuse can be addressed. The performance of the prototypes show that significant time and effort is saved compared to the state-of‐the‐art solution. The ability to do this effectively and efficiently during decommissioning will advance the oil the oil and gas industry’s transition toward a circular economy and help save on cost

Data warehouse structuring methodologies for efficient mining of Western Australian petroleum data sources

Representing the knowledge domain of a petroleum system is a complex problem. In the present study, logical modelling of shared attributes of resources industry entities (dimensions or objects) has been used for construction of a dynamic and time-variant metadata model. This work demonstrates effectiveness of multidimensional data modelling for petroleum industry, which will be further investigated for fine-grain data presentation and interpretation for quality knowledge discovery

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Smart grid interoperability use cases for extending electricity storage modeling within the IEC Common Information Model

Copyright @ 2012 IEEEThe IEC Common Information Model (CIM) is recognized as a core standard, supporting electricity transmission system interoperability. Packages of UML classes make up its domain ontology to enable a standardised abstraction of network topology and proprietary power system models. Since the early days of its design, the CIM has grown to reflect the widening scope and detail of utility information use cases as the desire to interoperate between a greater number of systems has increased. The cyber-physical nature of the smart grid places even greater demand upon the CIM to model future scenarios for power system operation and management that are starting to arise. Recent developments of modern electricity networks have begun to implement electricity storage (ES) technologies to provide ancillary balancing services, useful to grid integration of large-scale renewable energy systems. In response to this we investigate modeling of grid-scale electricity storage, by drawing on information use cases for future smart grid operational scenarios at National Grid, the GB Transmission System Operator. We find current structures within the CIM do not accommodate the informational requirements associated with novel ES systems and propose extensions to address this requirement.This study is supported by the UK National Grid and Brunel Universit

Research challenges in applying intelligent wireless sensors in the oil, gas and resources industries

The monitoring of oil, gas and resources plant performance and the operational environment through sensors allows for greater insight into potential safety problems and operational requirements. Such solutions promote a safe and healthy work environment for all stakeholders and optimized operations. Intelligent techniques and the monitoring of key historical operational properties can be used to realize certain characteristics and patterns in operation data. Such solutions may enhance operational visualization, foresight, forecasting and maintenance schedules for effective and efficient operation and maintenance. This optimizes plant safety, production, turnarounds, shutdowns and maintenance and improves error tolerance and recovery.However, the development of robust devices that are able to perform in these remote and hostile requirements along with the intelligent solutions to structure, store, process and retrieve this information are difficult to realize. This paper investigates the use of wireless sensors and the related intelligent solutions in the oil, gas and resource industries

Model Based Systems Engineering Approaches to Chemicals and Materials Manufacturing

Model-based systems engineering (MBSE) is part of a long-term trend toward model-centric approaches adopted by many engineering disciplines. This work establishes the need for an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. The origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) are discussed, along with the limitations of the current Process Systems Engineering (PSE) framework. The case is made for MBSE as a more generalizable and robust approach. Systems modeling strategies for MBSE are introduced, as well as a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. This work demonstrate the potential of MBSE approaches in chemical manufacturing by presenting two cases studies involving two different Active Pharmaceutical Ingredients (API), Atropine and Albuterol. The conclusion offers a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains

Knowledge management technology for integrated decision support systems in process industries

Premi extraordinari doctorat curs 2011-2012, àmbit d’Enginyeria IndustrialNowadays, factors such as globalization of trade, market uncertainty and fierce competition involve dwindling error margins in enterprises. Two key aspects for achieve it are the viability and the competitiveness of enterprises, which highly depend on the effectiveness for taking their decisions related to their manufacturing characteristics, such as economic efficiency, product quality, flexibility or reliability. For this reason, companies have taken the task, for many years, of develop better management information systems in order to help the decision makers to exploit data and models, with the final objective of discussing and improving decision-making. In this sense, decision support systems must be improved in order to deal with the large amount of available data and the heterogeneity of existing modeling approaches along the hierarchical levels in the enterprise structure. Hence, this thesis proposes the application of ontologies as a decision support tool, since they are increasingly seen as a key semantic technology for addressing heterogeneities and mitigating the problems they create and for enabling data mining by semantics-driven the knowledge processing. The aim of this thesis is to contribute to the development of decision support tools for the enterprise process industry. As a decision support tool, must be capable of become a robust model which interacts among the different decision hierarchical levels, providing a unified framework of data and information levels integration. On the other hand, this thesis also aims the improvement in the development of the ontologies. Firstly, a detailed state of the art about the different production process systems, knowledge management base on ontologies, as well as decision support systems is carried out. Based on this review, the specific thesis objectives are posed. Next, a methodology is proposed for the development and use of ontologies, based on the analysis and adaptation of previously existing methodologies. Such methodology is based on the improvement cycle (PSDA), allowing a better way to design, construct and apply domain ontologies. The second part of this thesis is devoted to the application of the different parts of the previously proposed methodology for the development of an ontological framework in the process industry domain concerning the strategic, tactical and operational decision levels. Next, the description of the decision areas in which the ontological framework is applied is presented. Namely, in the process control decision level, the coordination control is considered. Regarding scheduling decisions level, mathematical optimization approaches are applied. Finally, the distributed hierarchical decision level considers the mathematical optimization for decentralized supply chain networks is adopted. These decision areas and the performance of the proposed framework interaction are studied along the different case studies presented in the thesis. On the whole, this thesis represents a step forward toward the integration among the enterprise hierarchical levels, the process and enterprise standardization and improved procedures for decision-making. The aforementioned achievements are boosted by the application of semantic models, which are currently increasingly used.En la actualidad, factores como la globalización del comercio, la incertidumbre del mercado y la feroz competencia implican la disminución de los márgenes de error en las empresas. Dos aspectos claves para lograrlo son la viabilidad y la competitividad de las enterprisesm, que dependen en gran medida la eficacia para la toma de sus decisiones relacionadas con sus características de fabricación, tales como eficiencia económica, la calidad del producto, la flexibilidad y fiabilidad. Por esta razón, las empresas han dado a la tarea, desde hace muchos años, de desarrollar mejores sistemas de gestión de la información con el fin de ayudar a los tomadores de decisiones de explotación de datos y modelos, con el objetivo final de la discusión y mejorar la toma de decisiones. En este sentido, los sistemas de apoyo a las decisiones deben ser mejorados con el fin de hacer frente a la gran cantidad de datos disponibles y la heterogeneidad de los métodos de modelización existentes a lo largo de los niveles jerárquicos en la estructura de la empresa. Por lo tanto, esta tesis se propone la aplicación de ontologías como herramienta de apoyo a la decisión, ya que son cada vez más como una tecnología clave semántica para hacer frente a las heterogeneidades y la mitigación de los problemas que crean y para permitir la extracción de datos por la semántica impulsado la elaboración del conocimiento. El objetivo de esta tesis es contribuir al desarrollo de herramientas de apoyo para la industria de procesos empresariales. Como una herramienta de apoyo a la decisión, debe ser capaz de convertirse en un modelo sólido que interactúa entre los diferentes niveles de decisión jerárquica, proporcionando un marco unificado de datos e integración de los niveles de información. Por otra parte, esta tesis también tiene como objetivo la mejora en el desarrollo del área de ingeniería ontológica. En primer lugar, un estado detallado de la técnica sobre los diferentes sistemas de procesos de producción, la base de la gestión del conocimiento en ontologías, así como los sistemas de soporte de decisiones se ha llevado a cabo. Basado en esa revision, los objetivos específicos de la tesis se plantean. A continuación, se propone una metodología para el desarrollo y uso de ontologías, con base en el análisis y adaptación de las metodologías ya existentes. Dicha metodología se basa en el ciclo de mejora (PSDA), lo que permite una mejor manera de diseñar, construir y aplicar las ontologías de dominio. La segunda parte de esta tesis se dedica a la aplicación de las diferentes partes de la metodología propuesta anteriormente para el desarrollo de un marco ontológico en el ámbito de la industria de procesos relativos a los niveles de decisiones estratégicas, tácticas y operativas. A continuación, la descripción de las áreas de decisión en la que se aplica el marco ontológico se presenta. Es decir, en el nivel de decision de proceso de control, el control de la coordinación se considera. En cuanto al nivel de decisiones de programación de la producción, los métodos matemáticos de optimización se aplican. Finalmente, el nivel jerárquico distribuido decisión considera la optimización matemática de las redes descentralizadas de la cadena de suministro que se adopte. Estas áreas de decisión y el desempeño de la interacción marco propuesto se estudian a lo largo de los diferentes casos de estudio presentados en la tesis. En general, esta tesis supone un paso hacia adelante en la integración entre los niveles jerárquicos de la empresa, el proceso y la estandarización de la empresa y mejorar los procedimientos de toma de decisiones. Los logros mencionados se potencian mediante la aplicación de modelos semánticos, que actualmente se utilizan cada vez más.Award-winningPostprint (published version