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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.

Inhaltsverzeichnis

Frontmatter

Chapter 1. ISO 15926

Abstract
This chapter gives a brief description of the ISO15926 which presents the philosophy and basic concepts, and a brief review of ontology process and methodology for data integration and knowledge modelling. In the days of Big Data, a domain of special importance is the life-cycle data of processing facilities, for example the petrochemical installations, offshore platforms, and food processing. During the life-cycle of a facility, which may last for many decades, data are produced and are used for reference by hundreds of mutually incompatible applications that produce data in a multitude of formats. Where many of today’s systems require “data cleansing,” the aim of ISO 15926 is to map data at the source into the ISO 15926 format, with all due quality assurance before actual storage.
ISO 15926 is an attempt to develop ontology domains for data modeling and interoperability using the Semantic Web (RDF + OWL + FOL). It also includes an upper ontology for data integration and exchange. It is originally made for the Oil and Gas industry in order to prepare a common data model for long-term data integration, access, and sharing. It was developed with ISO TC184/SC4-Industrial Data by the EPISTLE consortium (1993–2003), and it is designed to support the evolution of data along time (ISO 15663-3, 2001). ISO 15926 is organized into a number of parts, each published separately.
Soumaya Yacout, Vahid Ebrahimipour

Chapter 2. Ontological Analysis and Engineering Standards: An Initial Study of IFC

Abstract
There is an increasing interest in developing ontological versions of engineering standards. In general, this amounts to restating a given standard in some ontological language like OWL. We observe that without an ontological analysis of the standard, the conversion neither improves the clarity of the standard nor facilitates its coherent application. In this chapter we begin to study the Industry Foundation Classes (IFC), a standard providing an open vendor-independent file format and data model for data interoperability and exchange for Architecture/Engineering/Construction and Facility Management. We first look at IFC and at an existing OWL version of IFC; then, we highlight the implicit assumptions and we apply ontological analysis to discuss how to best grasp the type/occurrence distinction in IFC. The goal is to show what has been done in IFC and the contribution of ontological analysis to help increasing the correct understanding of a standard. With this approach, we reach a deeper understanding, which can guide the translation from the original language to OWL with increased conceptual clarity while ensuring both logical coherence and ontological soundness.
Stefano Borgo, Emilio M. Sanfilippo, Aleksandra Šojić, Walter Terkaj

Chapter 3. FMEA, HAZID, and Ontologies

Abstract
This chapter is inspired by two sources—Trammell and Davis’ work on a fusion of HazOp and FMEA and extremely simple FMEA used by Airbus in the initial development phases. We discuss the use of ontologies to support efficient FMEA and HazId. The chapter starts with a discussion of FMEA and especially on the use of generic failure modes. In addition we also give a short introduction to HazId. After the introduction to FMEA we discuss the relationship between a control system’s FMEA and the system’s environment—how does a failure propagate to the environment to create harm. Here we also discuss the important concepts of generic fault trees and hazard lists, both heavily used in industry—which can be used to study how control systems’ failures propagate to the control system’s environment.
We then proceed by giving a short introduction to ontologies and how to create them. We show two examples—ontologies for a general control loop and for a simplified steam boiler. The chapter ends with a short discussion on what should be done by computers, using ontologies and computer programs, and what should be left to humans.
Tor Stålhane

Chapter 4. Ontology Development and Optimization for Data Integration and Decision-Making in Product Design and Obsolescence Management

Abstract
Product development in today’s global marketplace faces many challenges and pressures. Reducing development time, increasing quality and value, and reducing cost implications throughout the life cycle are critical. Strategies for dealing with rapidly changing technologies are very real and significant issues for product development as well. For low volume long-life complex products and systems in particular, such as those utilized by military and avionics applications, rapid advances in technologies have led to an escalation of time and costs challenges as manufacturers scramble to keep up with changes brought on by the obsolescence of components embodied in such systems. While some computer-based tools have been developed to aid product design and the management of obsolescence, benefits have been limited by issues associated with integrating heterogeneous sources of distributed information and knowledge. Data conflicts, data inexplicitness, incompleteness, inconsistency, and lacking information and knowledge needs for decisions associated with the management of obsolescence and product design continue. In recent years, ontology-based methods have presented new and promising approaches to manage knowledge in engineering, integrate multiple data resources, and facilitate the consideration of complex relations among concepts and properties for decision-making. In this chapter, manual and automatic ontology development and maintenance are introduced and ontologies can be optimized through identifying potential relations among distributed ontologies. Details of an ontology-based information system for data integration and decision support are also explained. Case studies are provided to illustrate the utilization of ontology with the proposed approaches to realize efficient product design and obsolescence management.
Xiaomeng Chang, Liyu Zheng, Janis Terpenny

Chapter 5. Fault Diagnosis System Based on Ontology for Fleet Case Reused

Abstract
Maintenance plays a key role by improving system availability, performance efficiency, and product quality. Condition based maintenance plus (CBM+) and Prognostics and Health Management (PHM) maintenance strategies propose new maintenance approaches in a “predict and prevent” view. In these anticipative approaches, early diagnosis plays a key role. Such a diagnosis is hard since only partial information is available. We propose to use the benefit of past event occurred on similar systems to help diagnosis. The originality of the approach lies in the consideration of systems that are not identical to the one under study. Indeed, similar systems are considered in order to gather more relevant and wider information to handle current diagnosis case. The level of similarity is controlled using an ontology in order to broaden or narrow the search.
The chapter proposes first a state of the art of the uses of a fleet for PHM strategies and more specifically for diagnosis. Secondly, the fleet-case-reused is presented within a feedback cycle in order to control the resulting information. Thirdly the ontology engineering and the embedded knowledge are described. Finally, using an application in the naval domain, the resulting software is presented and three scenarios show the advantages of the proposed approach.
Alexandre Voisin, Gabriela Medina-Oliva, Maxime Monnin, Jean-Baptiste Leger, Benoît Iung

Chapter 6. Integrating Cultural and Regulatory Factors in the Bowtie: Moving from Hand-Waving to Rigor

Abstract
Recent analyses of major incidents, such as BP’s Texas City and Macondo disasters and the loss of the space shuttle Columbia, have moved from considering immediate factors and basic organizational failings to including cultural issues. Culture is, however, even more difficult to incorporate into incident investigations and analyses than are organizational factors. This chapter provides a structured approach to analyzing individual, organizational and cultural/regulatory factors based upon the bowtie methodology, using well-defined rules to distinguish three levels of causation.
Level 1 (L1) analysis describes barriers implemented at the individual, team and immediate hardware level; Level 2 (L2) describes the organizational factors that support level one barriers; Level 3 (L3) describes the cultural and regulatory environment that ensures that the organization implements L2, thereby ensuring the integrity of L1 barriers. Incident investigation and analysis can be performed as a structured search for failed barriers at any or all of these three levels. Both organizational failure (L2) and problems with the safety culture and regulatory environment (L3) can be reliably and rigorously identified, rather than relying upon the intuitions of investigators.
This approach allows safety managers to support a range of approaches. First it enables them to identify and rank safety critical controls as those that manage the largest number of threats to integrity; this allows for a risk-based approach to auditing. Secondly the approach allows a rigorous definition of common mode failure with reference to the number of shared barriers at a higher level of analysis. Thirdly the detailed bowtie supports the investigation process by providing hypotheses about which controls have failed, including organizational, cultural, and regulatory factors.
Patrick Hudson, Timothy Hudson

Chapter 7. Addressing Uncertainty in Estimating the Cost for a Product-Service-System Delivering Availability: Epistemology and Ontology

Abstract
Recently there has been increase in the number of manufacturing firms offering service packages in support of their products, through performance-based or availability contracts. The delivery of “advanced services” by product-service-systems (PSS) is a knowledge-intensive socio-technical system in nature. Nonetheless, the challenges associated with addressing uncertainty in the context of estimating the cost of a PSS delivering availability need to be overcome. We present a system-based approach and discuss the uncertainties in modelling cost for a PSS. The aim is to demonstrate the limitations of using only quantitative analysis for modelling the uncertainty in estimating the cost of providing an advance service. Building on the epistemological foundation, we then discuss uncertainty in the context of ontology modelling and conclude with final remarks and directions for future research.
Yee Mey Goh, Linda Newnes, Ettore Settanni, Nils Thenent, Glenn Parry

Chapter 8. Ontology-Based Knowledge Platform to Support Equipment Health in Plant Operations

Abstract
Current energy production chains are carbon-based or derived from a carbon source (i.e., oil and gas), which have negative impacts on environment because of CO2 emission and other greenhouse gases. Growing world energy demand from fossil fuels plays a key role in the upward trend in CO2 emissions. Petrochemical and chemicals industries among other industrial sectors have much devastative influences on CO2 emissions through producing organic and inorganic products embodied carbon such as olefins, aromatics, ammonia, and carbon black or oil and gas combustions. The Organization of the Petroleum Exporting Countries (OPEC) states that the average portion of CO2 emissions from oil and gas usage is expected to double by 2050 (OPEC 2011). With current available technologies, the options for replacing fossil fuels or switching to less carbon fossil fuels are limited. These fossil fuels will likely remain to be the predominant source of energy in industry at least for this century. However, increasing costs for waste disposal and emissions control, growing international regulatory pressure, and increasing public demands for environmental quality are forcing nations to lay foundations for global agreement to curb CO2 emissions, for example the UN climate negotiations in Qatar, Kyoto Protocol, and OECD. To mitigate or eliminate adverse environmental impacts due to specific products and processes, national efforts have been conducted in order to switch to more efficient technologies, and to life cycle and system optimization approaches (DOE 2009). The Waste and Resources Action Program (WRAP) highlights the importance of product life cycles and operation management system’s optimization as two complementary approaches for achieving sustainable production process in the USA, the UK, the EU, and Japan (Brown et al. 2012). These two approaches attempt to reach optimal resource efficiency and sufficiency through waste reduction, lean production, industrial synergies, extended product lifetime, efficient use of equipment, and equipment lifetime optimization. For example, British Petroleum has recently launched the sustainability management system’s project in order to enhance HSE mitigation, and thus to earn back trust following the Gulf of Mexico accident in 2011 (BP 2011). Japan Society for the promotion of Science (JSPS) allocated research fellowships for sustainability engineering for equipment efficiency and lifetime optimization in the wake of Fukushima nuclear accident in 2011 (JSPS 2012). McKinsey & Company reported that according to Dow Chemical and Corning’s experience, low-carbon economy and energy efficiency are the new challenge of heavy industry companies in North America (McKinsey 2011). The Energy Academy Europe (EAE) has run a number of projects with the main themes of carbon capturing application and resource efficiency (EAE 2012).
Vahid Ebrahimipour, Soumaya Yacout

Backmatter

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