Jennifer A Harding - Academia.edu (original) (raw)

Papers by Jennifer A Harding

Procedia CIRP, 2016

With the explosive growth of digital data communications in synergistic operating networks and cl... more With the explosive growth of digital data communications in synergistic operating networks and cloud computing service, hyperconnected manufacturing collaboration systems face the challenges of extracting, processing, and analyzing data from multiple distributed web sources. Although semantic web technologies provide the solution to web data interoperability by storing the semantic web standard in relational databases for processing and analyzing of web-accessible heterogeneous digital data, web data storage and retrieval via the predefined schema of relational / SQL databases has become increasingly inefficient with the advent of big data. In response to this problem, the Hadoop Ecosystem System is being adopted to reduce the complexity of moving data to and from the big data cloud platform. This paper proposes a novel approach in a set of the Hadoop tools for information integration and interoperability across hyperconnected manufacturing collaboration systems. In the Hadoop approach, data is "Extracted" from the web sources, "Loaded" into a set of the NoSQL Hadoop Database (HBase) tables, and then "Transformed" and integrated into the desired format model with Hive's schema-on-read. A case study was conducted to illustrate that the Hadoop Extract-Load-Transform (ELT) approach for the syntax and semantics web data integration could be adopted across the global smartphone value chain.

Mechanical Systems and Signal Processing, 2017

It is common for original equipment manufacturers (OEMs) of high value products to provide mainte... more It is common for original equipment manufacturers (OEMs) of high value products to provide maintenance or service packages to customers to ensure their products are maintained at peak efficiency throughout their life. To quickly and efficiently plan for maintenance requirements, OEMs require accurate information about the use and wear of their products. In recent decades, the aerospace industry in particular has become expert in using real time data for the purpose of product monitoring and maintenance scheduling. Significant quantities of real time usage data from product monitoring are commonly generated and transmitted back to the OEMs, where diagnostic and prognostic analysis will be carried out. More recently, other industries such as construction and automotive, are also starting to develop capabilities in these areas and condition based maintenance (CBM) is increasing in popularity as a means of satisfying customers' demands. CBM requires constant monitoring of real time product data by the OEMs, however the biggest challenge for these industries, in particular construction, is the lack of accurate and real time understanding of how their products are being used possibly because of the complex supply chains which exist in construction projects. This research focuses on current dynamic data acquisition techniques for mobile hydraulic systems, in this case the use of a mobile inline particle contamination sensor; the aim was to assess suitability to achieve both diagnostic and prognostic requirements of Condition Based Maintenance. It concludes that hydraulic oil contamination analysis, namely detection of metallic particulates, offers a reliable way to measure real time wear of hydraulic components.

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture, 2015

Enterprises, especially virtual enterprises (VEs) are nowadays getting more knowledge intensive a... more Enterprises, especially virtual enterprises (VEs) are nowadays getting more knowledge intensive and adopting efficient Knowledge management (KM) systems to boost their competitiveness. The major challenge for KM for VEs is to acquire, extract and integrate new knowledge with the existing source. Ontologies have been proved to be one of the best tools for representing knowledge with class, role and other characteristics. It is imperative to accommodate the new knowledge in the current ontologies with logical consistencies as it is tedious and costly to construct new ontologies every time after acquiring new knowledge. This paper introduces a mechanism and a process to integrate new knowledge in to the current system (ontology). Separate methods have been adopted for fuzzy and concrete domain ontologies. The process starts by finding the semantic and structural similarities between the concepts using Wordnet and Description logic (DL). DL-based reasoning is used next to determine the position and relationships between the incoming and existing knowledge. The experimental results provided show the efficacy of the proposed Method.

Panetto/Interoperability for Enterprise Software and Applications, 2013

The concept of virtual enterprise is one of the most competitive industrial strategies for struct... more The concept of virtual enterprise is one of the most competitive industrial strategies for structuring and revitalising enterprises for the 21 st century. In such collaboration, enterprises temporarily share competences and resources for a particular business goal and disband when that window of opportunity is closed. The paradigm of the virtual enterprise is a predominant area of research and technological development for today's progressive industries.

Journal of Intelligent Manufacturing, 2014

Corporate memory is the total body of data, information and knowledge required to deliver the str... more Corporate memory is the total body of data, information and knowledge required to deliver the strategic aims and objectives of an organization. In the current market, the rapidly increasing volume of unstructured documents in the enterprises has brought the challenge of building an autonomic framework to acquire, represent, learn and maintain corporate memory, and efficiently reason from it to aid in knowledge discovery and reuse. The concept of semantic web is being introduced in the enterprises to structure information in a machine readable way and enhance the understandability of the disparate information. Due to the continual popularity of the semantic web, this paper develops a framework for corporate memory management on the semantic web. The proposed approach gleans information from the documents, converts into a semantic web resource using RDF and RDF Schema and then identifies relations among them using Latent Semantic Analysis (LSA) technique. The efficacy of the proposed approach is demonstrated through empirical experiments conducted on two case studies.

International Journal of Production Research, 2003

Manufacturing System Engineering (MSE) is a complex process generally performed by a multi-discip... more Manufacturing System Engineering (MSE) is a complex process generally performed by a multi-discipline project team. The Manufacturing System (MS) must satisfy many different requirements and objectives so compromises generally have to be made to achieve a balanced design for the new or re-engineered MS. Project team members must be aware when decisions are made which are significant to other team members. When teams are large and located in multiple sites, this can be very difficult to achieve, and intelligent support systems are necessary. The MSE Moderator is designed to monitor design decisions, evaluate their significance to individual project team members and communicate with any team members deemed necessary.

International Journal of Production Research, 2005

The paper deals with the conceptual design and development of an enterprise modeling and integrat... more The paper deals with the conceptual design and development of an enterprise modeling and integration framework using knowledge discovery and data mining. First, the paper briefly presents the background and current state-of-the-art of knowledge discovery in databases and data mining systems and projects. Next, enterprise knowledge engineering is dealt with. The paper suggests a novel approach of utilizing existing enterprise reference architectures, integration and modeling frameworks by the introduction of new enterprise views such as mining and knowledge views. An extension and a generic exploration of the information view that already exists within some enterprise models, are also proposed. The Zachman Framework for Enterprise Architecture is also outlined against the existing architectures and the proposed enterprise framework. The main contribution of this paper is the identification and definition of a common knowledge enterprise model which represents an original combination between the previous projects on enterprise architectures and the Object Management Group (OMG) models and standards. The identified common knowledge enterprise model has therefore been designed using the OMG's Model-Driven Architecture (MDA) and Common Warehouse MetaModel (CWM), and it also follows the RM-ODP (ISO/OSI). It has been partially implemented in Java TM , Enterprise JavaBeans (EJB) and Corba/IDL. Finally, the advantages and limitations of the proposed enterprise model are outlined.

Proceedings of the Institution …, 2007

This paper presents a deep investigation and an interdisciplinary analysis of the collaborative n... more This paper presents a deep investigation and an interdisciplinary analysis of the collaborative networked enterprise engineering issues and modelling approaches related to the relevant aspects of the semantic web technology and knowledge strategies. The paper also suggests a novel framework based on ontology metamodelling, knowledge model discovery, and semantic web infrastructures, architectures, languages, and systems. The main aim of the research enclosed in this paper is to bridge the gaps between enterprise engineering, modelling, and especially networking by intensively applying semantic web technology based on ontology conceptual representations and knowledge discovery. The ontological modelling approaches together with knowledge strategies such as discovery (data mining) have become promising for future enterprise computing systems. The related reported research deals with the conceptual definition of a semantic-driven framework and a manufacturing enterprise metamodel (ME M) using ontology, knowledge-driven object models, standards, and architectural approaches applied to collaborative networked enterprises. The conceptual semantic framework and related issues discussed in this paper may contribute towards new approaches of enterprise systems engineering and networking as well as applied standard and referenced ontological models.

ABSTRACT The requirements for the interoperability of semantics and knowledge have become increas... more ABSTRACT The requirements for the interoperability of semantics and knowledge have become increasingly important in Product Lifecycle Management (PLM), in the drive towards knowledge-driven decision support in the manufacturing industry. This article presents a novel concept, based on the Model Driven Architecture (MDA). The concept has been implemented under the Interoperable Manufacturing Knowledge Systems (IMKS) project in order to understand the extent to which manufacturing system interoperability can be supported using radically new methods of knowledge sharing. The concept exploits the capabilities of semantically well-defined core concepts formalised in a Common Logic-based ontology language. The core semantics can be specialised to configure multiple application-specific knowledge bases, as well as product and manufacturing information platforms. Furthermore, the utilisation of the expressive ontology language and the generic nature of core concepts help support the specification of system mechanisms to enable the verification of knowledge across multiple platforms. An experimental demonstration, using a test case based on the design and manufacture of an aerospace part, has been realised. This has led to the identification of several benefits of the approach, its current limitations as well as the areas to be considered for further work.

Journal of Intelligent …, 2009

In modern manufacturing environments, vast amounts of data are collected in database management s... more In modern manufacturing environments, vast amounts of data are collected in database management systems and data warehouses from all involved areas, such as product and process design, assembly, materials planning, quality control, scheduling, maintenance, fault detection and so on. Data mining has emerged as an important tool for knowledge acquisition in manufacturing databases. This paper reviews the literature dealing with knowledge discovery and data mining applications in the broad domain of manufacturing with an special emphasis on the type of functions to be performed on data. The major data mining functions to be performed include characterization and description, association, classification, prediction, clustering and evolution analysis. The papers reviewed have therefore been categorized in these five categories. It has been shown that there is a rapid growth in the application of data mining in the context of manufacturing processes and enterprises in the last 3 years. This review reveals the progressive applications and existing gaps identified in the context of data mining in manufacturing. A novel text mining approach has also been applied to the abstracts and keywords of 150 identified literatures to identify the research gaps and find the linkages between knowledge area, knowledge type and data mining tools and techniques applied.

Proceedings of The Institution of Mechanical Engineers Part B-journal of Engineering Manufacture, 2006

Modern manufacturing systems equipped with computerized data logging systems collect large volume... more Modern manufacturing systems equipped with computerized data logging systems collect large volumes of data in real time. The data may contain valuable information for operation and control strategies as well as providing knowledge of normal and abnormal operational patterns. Knowledge discovery in databases can be applied to these data to unearth hidden, unknown, representable, and ultimately useful knowledge. Data mining offers tools for discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Extraction of previously unknown, meaningful information from manufacturing databases provides knowledge that may benefit many application areas within the enterprise, for example improving design or fine tuning production processes. This paper examines the application of association rules to manufacturing databases to extract useful information about a manufacturing system's capabilities and its constraints. The quality of each identified rule is tested and, from numerous rules, only those that are statistically very strong and contain substantial design information are selected. The final set of extracted rules contains very interesting information relating to the geometry of the product and also indicates where limitations exist for improvement of the manufacturing processes involved in the production of complex geometric shapes.

Many construction companies conduct reviews on project completion to enhance learning and to fulf... more Many construction companies conduct reviews on project completion to enhance learning and to fulfil quality management procedures. Often these reports are filed away never to be seen again. This means that potentially important knowledge that may assist other project teams is not exploited. This paper investigates whether Knowledge Discovery from Text (KDT) and text mining (TM) could be used to "discover" useful knowledge from such reports. Text mining avoids the need to manually search a vast number of reports, potentially of different formats and foci, to seek trends that may be useful for current and future projects. Pilot tests were used to analyse 48 post-project review reports. The reports were first reviewed manually to identify key themes. They were then analysed using text mining software to investigate whether text mining could identify trends and uncover useful knowledge from the reports. Pilot tests succeeded in finding common occurrences across different projects that were previously unknown. Text mining could provide a potential solution and would aid project teams to learn from previous projects. However, a lot of work is currently required before the text mining tests are conducted and the results need to be examined carefully by those with domain knowledge to validate the results obtained.

International Journal of Production Research, Dec 1, 2011

A major issue in any multidiscipline collaborative project is how to best share and simultaneousl... more A major issue in any multidiscipline collaborative project is how to best share and simultaneously exploit different types of expertise, without duplicating efforts or inadvertently causing conflicts or loss of efficiency through misunderstanding of individual or shared goals. Moderators are knowledge based systems designed to support collaborative teams by raising awareness of potential problems or conflicts. However, the functioning of a Moderator is limited by the knowledge it has about the team members.

Proceedings of the …, 2006

Capturing and reusing knowledge of best practices has been identified as one of the requirements ... more Capturing and reusing knowledge of best practices has been identified as one of the requirements for next-generation product development. Knowledge identification is therefore already being done to some degree in many organizations, through instruction manuals or 'how to' guidelines. However, this is only a first step, as to fully exploit valuable knowledge, best practices must be identified and shared. A detailed review of previous research in best practice knowledge management shows that the method of modelling best practice knowledge and the resulting model structure are critically important for the successful reuse of best practice knowledge. Yet, to date, only limited research has been focused on these aspects. This paper therefore presents research into a methodology to determine ways for better communication, sharing, and reuse of best/good practice knowledge. The proposed methodology has been divided into two parts: firstly, the identification of best practices for product development, and secondly, the structuring of best practice knowledge for effective sharing and reuse. This methodology encourages the adoption of best practices by providing knowledge about both process and implementation elements. This makes the explicit knowledge easier to find and reuse. Once a best practice is found to suit current requirements and circumstances, an expert who has identified and used the best practice can also be contacted to gain additional knowledge/information. This helps to address the challenges posed by 'tacit' knowledge, which cannot easily be shared within the knowledge base.

This paper describes an approach for reusing engineering design knowledge. Many previous design k... more This paper describes an approach for reusing engineering design knowledge. Many previous design knowledge reuse systems focus exclusively on geometrical data, which is often not applicable in early design stages. The proposed methodology provides an integrated design knowledge reuse framework, bringing together elements of best practice reuse, design rationale capture and knowledge-based support in a single coherent framework. Best practices are reused through the process model. Rationale is supported by product information, which is retrieved through links to design process tasks. Knowledge-based methods are supported by a common design data model, which serves as a single source of design data to support the design process. By using the design process as the basis for knowledge structuring and retrieval, it serves the dual purpose of design process capture and knowledge reuse: capturing and formalising the rationale that underpins the design process, and providing a framework thro...

Computers in Industry, 2001

Market driven strategies encourage enterprises to produce products that customers want to buy, an... more Market driven strategies encourage enterprises to produce products that customers want to buy, and therefore can improve an enterprise"s market position. Few organisations make effective use of market, competitor and customer information. Information modelling and intelligent support tools help define product specifications focused on fulfilling customer requirements and facilitating information sharing between members of extended design teams. Design effort can be targeted at particular product features, which yield maximum benefits for customer satisfaction. The Market Driven Design System provides comprehensive, intelligent support, meeting the challenges of effectively modelling, using and sharing valuable, yet imprecise, non-technical market information during product design.

Robotics and Computer- …, 2008

This paper proposes a knowledge representation method that supports greater reuse of manufacturin... more This paper proposes a knowledge representation method that supports greater reuse of manufacturing knowledge in design. The method draws on recent research into objectoriented product and manufacturing models, and problem solving agents. A research platform is proposed, and the results of a test case (based on a simplified jet engine combustion chamber) are described. The paper concludes with three basic principles of reuse, i.e. product/process separation, procedural/declarative knowledge separation, and guidelines for the optimum location of rules and constraints within product/manufacturing models.

Computers in Industry, 2013

The requirements for the interoperability of semantics and knowledge have become increasingly imp... more The requirements for the interoperability of semantics and knowledge have become increasingly important in Product Lifecycle Management (PLM), in the drive towards knowledgedriven decision support in the manufacturing industry. This article presents a novel concept, based on the Model Driven Architecture (MDA). The concept has been implemented under the Interoperable Manufacturing Knowledge Systems (IMKS) project in order to understand the extent to which manufacturing system interoperability can be supported using radically new methods of knowledge sharing. The concept exploits the capabilities of semantically well-defined core concepts formalised in a Common Logic-based ontology language. The core semantics can be specialised to configure multiple application-specific knowledge bases, as well as product and manufacturing information platforms. Furthermore, the utilisation of the expressive ontology language and the generic nature of core concepts help support the specification of system mechanisms to enable the verification of knowledge across multiple platforms. An experimental demonstration, using a test case based on the design and manufacture of an aerospace part, has been realised. This has led to the identification of several benefits of the approach, its current limitations as well as areas to be considered for further work.

Enterprise Interoperability IV, 2010