An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Student-centred quality improvement systems in manufacturing engineering higher education

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Recent changes within British higher education have led to a refocus as to the purpose of such education. This movement has been particularly noticeable in the engineering disciplines. These changes have involved a move towards a more 'transformational' objective, where the emphasis is on the development of the full range of learning abilities within a student coupled with an external, or 'real world', orientation. To develop such learning abilities requires specific educational approaches that are based on student-centred processes and the preparation for lifelong learning. This new purpose, and its inherent educational methods, has implications for the type of quality improvement systems adopted. Robust approaches developed in manufacturing industries have been identified, and the thesis argues that quality systems based on developments in these industries can be used in higher education to create a culture that engenders this positive learning approach. This involves a move away from passive, quantitative quality monitoring systems that focus on the 'product' of learning, and move towards more qualitative, active and dynamic department-wide quality improvement systems that focus on the developmental 'process'. Traditional methods of addressing quality in higher education departments can be seen to focus on rudimentary control mechanisms, where action is post-process and reactive, and where the feedback loop often not closed, i.e. preventative and corrective actions, when identified, are not initiated. Such approaches add very little to the purpose of higher education (i.e. developing the range of 'transformational' learning abilities), as there is an overemphasis on evaluation and not enough emphasis on enhancement, development and preparation for continuous learning. The main thesis, therefore, links learning theory to quality theory, via the concepts of development cycles, lifelong learning and continuous improvement. To ascertain the validity of the theses required a research methodology that was based on an in-depth longitudinal action/applied research case study. The research involved a three and a half year study of the quality improvement systems of a manufacturing engineering department of a British university. The research introduced and investigated a strategy that would result in a move from the 'post-process/passive' student involvement to 'in-process/ active'. The case study found that the thesis was valid, in that particular students and members of staff adopted the quality improvement system (i.e. a change in observable behaviour). The contribution to knowledge involves the examination of the interaction between departmental culture and systems, where a 'cultural shift' is necessary involving (i) a change in the role of the undergraduate student (i.e. from passive members in the process, to central participants in the creation and improvement of quality), and (ii) a change in the focus of quality (i.e. from checking that learning was taken place, to promoting and preparing students for lifelong learning).Funding was obtained from the Engineering and Physical Sciences Research Council

Design And Implementation Of Expert Systems In Trace Metal Analysis

Atomic Absorption Expert (AAexpert) is an expert system dealing with automated analysis for trace metals by atomic absorption spectrometry (AAS). This program consists of several stand-alone expert systems, each performing a specific task associated with the analysis.;In this thesis, the design and implementation of three important modules of AAexpert have been studied. These are (a) AAdiagnosis - detects problems associated with analytical data of poor quality and instrument malfunction, (b) AA-Quality Control (AA-QC) - provides a real-time assessment of the quality of measured absorbance values, and (c) AAmethods - selects a method of analysis from a method selection database and a rulebase.;The focal points of the work presented here are (a) the transfer of human expertise to a computer program, and (b) the requirements for totally unattended automated analyses.;In the transfer of human expertise, knowledge acquisition has been stressed and the development of a knowledge table has been described. The developer of the expert system assembles the chemical knowledge as case histories. The knowledge table generates rules that represent the acquired knowledge. This approach of knowledge acquisition would greatly assist chemists in the generation, expansion, and portability of their knowledge bases. The implementation of the knowledge table has been demonstrated in AAdiagnosis in which the developer gathers knowledge as a matrix of symptoms (rows) and the underlying causes (columns).;The goal towards totally automated analysis has been described in AA-QC. This module closely interacts with AAcontrol, a module responsible for solution handling and acquisition of data by flame AAS (FAAS). It has been shown that by modelling the detector response (the absorption profile), it is possible to detect a few common and simple problems associated with analysis by FAAS. AA-QC uses a training set based on the numerical analysis of the absorption profile for a set of standard solutions. AA-QC uses production rules to detect problems associated with the analysis.;The use of an expert database system has been studied in AAmethods. The use of rules to suggest a closest match method of analysis contained in an expert database has been demonstrated

ISO roadmap for software products

The complex nature of ISO 9001 standards has been an obvious limitation for implementation. ISO 9001 accreditation provides external and internal advantages. The external benefits include increased market access and customer confidence. The internal advantages include quality improvements in both the product and process. However, this research demonstrates that the current ISO guideline for software products has not completely fulfilled the expectations in its implementation within the software organisation. Numerous companies are experiencing difficulties with ISO implementation and maintenance. Based on the analysis provided by this research, poor communication and an unsystematic approach were identified as the main current problems associated with ISO implementation. In addition, it also shows that the organisations often underestimate internal organisation factors, such as resistance to change. The existing difficulties in maintaining applied quality systems have resulted in a lack of control and monitoring. This thesis introduces an ISO Roadmap and corresponding Checklist for software products. The main goal of this ISO Roadmap is to provide enhanced transparency and assistance in the application of ISO 90003:2004 in order to make ISO implementation more manageable, visible and understandable for all people involved within the organisation. The result of this research provides the software industry with an initial step towards better systematisation and control for ISO implementations. This should help the software industry to better navigate through the ‘ISO jungle’ and facilitate an improved approach for implementation and maintenance activities. This research relied heavily on the development of the literature review, ISO 90003 standards, and knowledge from interviewees ... [cont.]

State Feedback of Complex Systems Using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems

State Feedback of Complex Systems using Fuzzy Cognitive Maps

Complex systems have become a research area with increasing interest over the last years. The emergence of new technologies, the increase in computational power with reduced resources and cost, the integration of the physical world with computer based systems has created the possibility of significantly improving the quality of life of humans. While a significant degree of automation within these systems exists and has been provided in the past decade with examples of the smart homes and energy efficient buildings, a paradigm shift towards autonomy has been noted. The need for autonomy requires the extraction of a model; while a strict mathematical formulation usually exists for the individual subsystems, finding a complete mathematical formulation for the complex systems is a near impossible task to accomplish. For this reason, methods such as the Fuzzy Cognitive Maps (FCM) have emerged that are able to provide with a description of the complex system. The system description results from empirical observations made from experts in the related subject – integration of expert’s knowledge – that provide the required cause-effect relations between the interacting components that the FCM needs in order to be formulated. Learning methods are employed that are able to improve the formulated model based on measurements from the actual system. The FCM method, that is able to inherently integrate uncertainties, is able to provide an adequate model for the study of a complex system. With the required system model, the next step towards the development of a autonomous systems is the creation of a control scheme. While FCM can provide with a system model, the system representation proves inadequate to be utilized to design classic model based controllers that require a state space or frequency domain representation. In state space representation, the state vector contains the variables of the system that can describe enough about the system to determine its future behavior in absence of external variables. Thus, within the components – the nodes of the FCM, ideally those can be identified that constitute the state vector of the system. In this work the authors propose the creation of a state feedback control law of complex systems via Fuzzy Cognitive Maps. Given the FCM representation of a system, initially the components-states of the system are identified. Given the identified states, a FCM representation of the controller occurs where the controller parameters are the weights of the cause-effect relations of the system. The FCM of the system then is augmented with the FCM of the controller. An example of the proposed methodology is given via the use of the cart-pendulum system, a common benchmark system for testing the efficiency of control systems

Project for a European technological platform on organic agriculture: Vision for an Organic Food and Farming Research Agenda to 2025

Facing global problems like food security, the unsustainable use of natural resources, the degradation of soils and biodiversity as well as climate change, international experts demand a strategy change in agriculture and in agricultural research. Such a change encompasses not only re-establishing principles like closing cycles in agro-ecosystems, making best use of regulating and supporting ecosystems services but also making use of indigenous or tacit knowledge of farmer communities in addition to technological progress. The reports of the “Millennium Ecosystem Assessment” and of the IAASTD highlighted the need for this change in 2005 and 2008. Influenced by these recommendations, the potential of organic food and farming systems have to be assessed for the future of agriculture. Consequently, it is important to debate the future development of organic food and farming systems. Does organic agriculture stick to a niche strategy of producing high quality food for an elite of consumers? Or is organic farming a main stream strategy for feeding the world by minimising the negative impacts on the environment? Are these two objectives combinable? Organic farming is based on management strategies which are crucial for sustainable agriculture: The productivity of crops is maintained by closed circuits of nutrients and biomass, depending on multiple interfaces between livestock and cropping systems. Crop rotations integrate leguminous plants in order to make agriculture independent from external nitrogen supply and consequently reducing energy consumption and GHG emissions. Furthermore, the management and increase of biodiversity is an inherent approach of organic agriculture in order to control pest and diseases, as well as the increase of soil fertility in order to maintain high yields. And finally, organic farming has always used indigenous and tacit knowledge. Eco-functional intensification will be the major challenge of future agriculture. That’s why the IFOAM-EU group published a vision for the future of organic food and farming systems (see www.organicresearch.org) and made a first step towards its implementation by setting up a technology platform called “organics”

Performance measurement : challenges for tomorrow

This paper demonstrates that the context within which performance measurement is used is changing. The key questions posed are: Is performance measurement ready for the emerging context? What are the gaps in our knowledge? and Which lines of enquiry do we need to pursue? A literature synthesis conducted by a team of multidisciplinary researchers charts the evolution of the performance-measurement literature and identifies that the literature largely follows the emerging business and global trends. The ensuing discussion introduces the currently emerging and predicted future trends and explores how current knowledge on performance measurement may deal with the emerging context. This results in identification of specific challenges for performance measurement within a holistic systems-based framework. The principle limitation of the paper is that it covers a broad literature base without in-depth analysis of a particular aspect of performance measurement. However, this weakness is also the strength of the paper. What is perhaps most significant is that there is a need for rethinking how we research the field of performance measurement by taking a holistic systems-based approach, recognizing the integrated and concurrent nature of challenges that the practitioners, and consequently the field, face

Implementation Action Plan for organic food and farming research

The Implementation Action Plan completes TP Organics’ trilogy of key documents of the Research Vision to 2025 (Niggli et al 2008) and the Strategic Research Agenda (Schmid et al 2009). The Implementation Action Plan addresses important areas for a successful implementation of the Strategic Research Agenda. It explores the strength of Europe’s organic sector on the world stage with about one quarter of the world’s organic agricultural land in 2008 and accounting for more than half of the global organic market. The aims and objectives of organic farming reflect a broad range of societal demands on the multiple roles of agriculture and food production of not only producing commodities but also ecosystem services. These are important for Europe’s economic success, the resilience of its farms and prosperity in its rural areas. The organic sector is a leading market for quality and authenticity: values at the heart of European food culture. Innovation is important across the EU economy, and no less so within the organic sector. The Implementation Action Plan devotes its third chapter to considering how innovation can be stimulated through organic food and farming research and, crucially, translated into changes in business and agricultural practice. TP Organics argues for a broad understanding of innovation that includes technology, know-how and social/organisational innovations. Accordingly, innovation can involve different actors throughout the food sector. Many examples illustrate innovations in the organic sector includign and beyond technology. The various restrictions imposed by organic standards have driven change and turned organic farms and food businesses into creative living laboratories for smart and green innovations and the sector will continue to generate new examples. The research topics proposed by TP Organics in the Strategic Research Agenda can drive innovation in areas as wide ranging as production practices for crops, technologies for livestock, food processing, quality management, on-farm renewable energy or insights into the effects of consumption of organic products on disease and wellbeing and life style of citizens. Importantly, many approaches developed within the sector are relevant and useful beyond the specific sector. The fourth chapter addresses knowledge management in organic agriculture, focusing on the further development of participatory research methods. Participatory (or trans-disciplinary) models recognise the worth and importance of different forms of knowledge and reduced boundaries between the generators and the users of knowledge, while respecting and benefitting from transparent division of tasks. The emphasis on joint creation and exchange of knowledge makes them valuable as part of a knowledge management toolkit as they have the capacity to enhance the translation of research outcomes into practical changes and lead to real-world progress. The Implementation Action Plan argues for the wider application of participatory methods in publicly-funded research and also proposes some criteria for evaluating participatory research, such as the involvement and satisfaction of stakeholders as well as real improvements in sustainability and delivery of public goods/services. European agriculture faces specific challenges but at the same time Europe has a unique potential for the development of agro-ecology based solutions that must be supported through well focused research. TP Organics believes that the most effective approaches in agriculture and food research will be systems-based, multi- and trans-disciplinary, and that in the development of research priorities, the interconnections between biodiversity, dietary diversity, functional diversity and health must be taken into account. Chapter five of the action plan identifies six themes which could be used to organise research and innovation activities in agriculture under Europe’s 8th Framework Programme on Research Cooperation: • Eco-functional intensification – A new area of agricultural research which aims to harness beneficial activities of the ecosystem to increase productivity in agriculture. • The economics of high output / low input farming Developing reliable economic and environmental assessments of new recycling, renewable-based and efficiency-boosting technologies for agriculture. • Health care schemes for livestock Shifting from therapeutics to livestock health care schemes based on good husbandry and disease prevention. • Resilience and “sustainagility” Dealing with a more rapidly changing environment by focusing on ‘adaptive capacity’ to help build resilience of farmers, farms and production methods. • From farm diversity to food diversity and health and wellbeing of citizens Building on existing initiatives to reconnect consumers and producers, use a ‘whole food chain’ approach to improve availability of natural and authentic foods. • Creating centres of innovation in farming communities A network of centres in Europe applying and developing trans-disciplinary and participatory scientific approaches to support innovation among farmers and SMEs and improving research capacities across Europe

Green BPM as a business-oriented discipline : a systematic mapping study and research agenda

Green Business Process Management (BPM) focuses on the ecological impact of business processes. This article provides a systematic mapping study of Green BPM literature to evaluate five attributes of the Green BPM research area: (1) scope, (2) disciplines, (3) accountability, (4) researchers and (5) quality control. The results allow developing a research agenda to enhance Green BPM as an approach for environmentally sustainable organizations. We rely on a dichotomy of knowledge production to present research directives relevant for both academics and practitioners in order to help close a rigor-relevance gap. The involvement of both communities is crucial for Green BPM to advance as an applied, business-oriented discipline