This book presents cutting-edge applications of, and up-to-date research on, ontology engineering techniques in the physical asset integrity domain. Though a survey of state-of-the-art theory and methods on ontology engineering, the authors emphasize essential topics including data integration modeling, knowledge representation, and semantic interpretation. The book also reflects novel topics dealing with the advanced problems of physical asset integrity applications such as heterogeneity, data inconsistency, and interoperability existing in design and utilization. With a distinctive focus on applications relevant in heavy industry, Ontology Modeling in Physical Asset Integrity Management is ideal for practicing industrial and mechanical engineers working in the field, as well as researchers and graduate concerned with ontology engineering in physical systems life cycles.
This book is open access under a CC BY 4.0 license.This book presents results relevant in the manufacturing research field, that are mainly aimed at closing the gap between the academic investigation and the industrial application, in collaboration with manufacturing companies. Several hardware and software prototypes represent the key outcome of the scientific contributions that can be grouped into five main areas, representing different perspectives of the factory domain:1) Evolutionary and reconfigurable factories to cope with dynamic production contexts characterized by evolving demand and technologies, products and processes.2) Factories for sustainable production, asking for energy efficiency, low environmental impact products and processes, new de-production logics, sustainable logistics.3) Factories for the People who need new kinds of interactions between production processes, machines, and human beings to offer a more comfortable and stimulating working environment.4) Factories for customized products that will be more and more tailored to the final user’s needs and sold at cost-effective prices.5) High performance factories to yield the due production while minimizing the inefficiencies caused by failures, management problems, maintenance.This books is primarily targeted to academic researchers and industrial practitioners in the manufacturing domain.
Big data and artificial intelligence (AI) are at the forefront of technological advances that represent a potential transformational mega-trend—a new multipolar and innovative disruption. These technologies, and their associated management paradigm, are already rapidly impacting many industries and occupations, but in some sectors, the change is just beginning. Innovating ahead of emerging technologies is the new imperative for any organization that aspires to succeed in the next decade. Faced with the power of this AI movement, it is imperative to understand the dynamics and new codes required by the disruption and to adapt accordingly. AI and Big Data’s Potential for Disruptive Innovation provides emerging research exploring the theoretical and practical aspects of successfully implementing new and innovative technologies in a variety of sectors including business, transportation, and healthcare. Featuring coverage on a broad range of topics such as semantic mapping, ethics in AI, and big data governance, this book is ideally designed for IT specialists, industry professionals, managers, executives, researchers, scientists, and engineers seeking current research on the production of new and innovative mechanization and its disruptions.
This book aims to facilitate and improve development work related to all documents and information required by functional safety standards. Proof of Compliance (PoC) is important for the assessor and certification bodies when called up to confirm that the manufacturer has developed a software system according to the required safety standards. While PoC documents add functionality to the product neither for the developer nor for the customer, they do add confidence and trust to the product and ease certification, and as such are important for the product’s value. In spite of this added value, the documentation needed for PoC is often developed late in the project and in a haphazard manner. This book aims at developers, assessors, certification bodies, and purchasers of safety instrumented systems and informs the reader about the most important PoC documents. A typical PoC documentation encompasses 50 to 200 documents, several of which are named in the safety standards (e.g., 82 documents in IEC 61508:2010 series, 101 documents in EN 5012X series and 106 work products in ISO 26262:2018 series). These documents also include further references, typically one to twenty of them, and the total number of pages developed by the manufacturer varies between 2000 and 10000 pages. The book provides guidance and examples what to include in the relevant plans and documents.
This book gathers the peer-reviewed papers presented at the seventh edition of the international workshop "Service Orientation in Holonic and Multi-Agent Manufacturing - SOHOMA'17", held on October 19-20, 2017 and organized by the University of Nantes, France in collaboration with the CIMR Research Centre in Computer Integrated Manufacturing and Robotics at the University Politehnica of Bucharest, Romania, the LAMIH Laboratory of Industrial and Human Automation Control, Mechanical Engineering and Computer Science at the University of Valenciennes and Hainaut-Cambrésis, France and the CRAN Research Centre for Automatic Control, Nancy at the University of Lorraine, France. The main objective of SOHOMA'17 was to foster innovation in smart and sustainable manufacturing and logistics systems and in this context to promote concepts, methods and solutions addressing trends in service orientation of agent-based control technologies with distributed intelligence. The book is organized in eight parts, each with a number of chapters describing research in current domains of the digital transformation in manufacturing and trends in future service and computing oriented manufacturing control: Part 1: Advanced Manufacturing Control, Part 2: Big Data Management, Part 3: Cyber-Physical Production Systems, Part 4: Cloud- and Cyber-Physical Systems for Smart and Sustainable Manufacturing, Part 5: Simulation for Physical Internet and Intelligent & Sustainable Logistics Systems, Part 6: Formal Methods and Advanced Scheduling for Future Industrial Systems, Part 7: Applications and Demonstrators, Part 8: Production and Logistic Control Systems. The contributions focus on how the digital transformation, such as the one advocated by "Industry 4.0" or "Industry of the future" concepts, can improve the maintainability and the sustainability of manufacturing processes, products, and logistics. Digital transformation relates to the interaction between the physical and informational worlds and is realized by virtualization of products, processes and resources managed as services.
In the age of digitalization and the fourth industrial revolution, predictive maintenance is becoming increasingly important as a proactive maintenance type. Despite the economic benefits that predictive maintenance generates for companies, its practical application is still in its early stages. This is often due to two prevailing challenges. First, there is a deficiency of knowledge about predictive maintenance and its concrete realization. Second, there is a lack of high quality and rich data of historical machine failures. To increase the representativeness of data, data from several similar machines (i.e. a fleet) should be considered. To foster the effective implementation of predictive maintenance, supportive guidance in the realization of a predictive maintenance project is needed. For this reason, this dissertation presents a process reference model and a development method for fleet prognostics. The process reference model describes a comprehensive and application-independent view of the complete predictive maintenance process. The model is supplemented by the fleet prognostic development method. To address the specific characteristics of the fleet, a systematic process is depicted which provides a means to assess the heterogeneity of the fleet from a data-driven perspective and simplifies the design of an algorithm considering fleet data. Finally, the applicability and value of the research results are demonstrated with three industrial cases
AN AUTHORITATIVE GUIDE THAT EXPLAINS THE EFFECTIVENESS AND IMPLEMENTATION OF BOW TIE ANALYSIS, A QUALITATIVE RISK ASSESSMENT AND BARRIER MANAGEMENT METHODOLOGY From a collaborative effort of the Center for Chemical Process Safety (CCPS) and the Energy Institute (EI) comes an invaluable book that puts the focus on a specific qualitative risk management methodology – bow tie barrier analysis. The book contains practical advice for conducting an effective bow tie analysis and offers guidance for creating bow tie diagrams for process safety and risk management. Bow Ties in Risk Management clearly shows how bow tie analysis and diagrams fit into an overall process safety and risk management framework. Implementing the methods outlined in this book will improve the quality of bow tie analysis and bow tie diagrams across an organization and the industry. This important guide: Explains the proven concept of bow tie barrier analysis for the preventing and mitigation of incident pathways, especially related to major accidents Shows how to avoid common pitfalls and is filled with real-world examples Explains the practical application of the bow tie method throughout an organization Reveals how to treat human and organizational factors in a sound and practical manner Includes additional material available online Although this book is written primarily for anyone involved with or responsible for managing process safety risks, this book is applicable to anyone using bow tie risk management practices in other safety and environmental or Enterprise Risk Management applications. It is designed for a wide audience, from beginners with little to no background in barrier management, to experienced professionals who may already be familiar with bow ties, their elements, the methodology, and their relation to risk management. The missions of both the CCPS and EI include developing and disseminating knowledge, skills, and good practices to protect people, property and the environment by bringing the best knowledge and practices to industry, academia, governments and the public around the world through collective wisdom, tools, training and expertise. The CCPS has been at the forefront of documenting and sharing important process safety risk assessment methodologies for more than 30 years. The EI's Technical Work Program addresses the depth and breadth of the energy sector, from fuels and fuels distribution to health and safety, sustainability and the environment. The EI program provides cost-effective, value-adding knowledge on key current and future international issues affecting those in the energy sector.
Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value. - Chart an overview of the techniques and tools of machine learning including all the non-technological aspects necessary to be successful - Gain practical understanding of machine learning used in oil and gas operations through contributed case studies - Learn change management skills that will help gain confidence in pursuing the technology - Understand the workflow of a full-scale project and where machine learning benefits (and where it does not)
