Strategic view of an assets health index for making long-term decisions in different industries

Libro en Open AccessAn Asset Health Index (AHI) is a tool that processes data about asset’s condition. That index is intended to explore if alterations can be generated in the health of the asset along its life cycle. These data can be obtained during the asset’s operation, but they can also come from other information sources such as geographical information systems, supplier’s reliability records, relevant external agent’s records, etc. The tool (AHI) provides an objective point of view in order to justify, for instance, the extension of an asset useful life, or in order to identify which assets from a fleet are candidates for an early replacement as a consequence of a premature aging. This paper develops a model applicable to different classes of equipment and industrial sectors. A review of the main cases where the asset health index has been applied is included. Likewise, advantages and disadvantages in the application of this kind of tools are revealed, providing a guide for a research line related to the general application of this tool

Determinación de estrategia de optimización del ciclo de vida de activos de alta capitalización en la industria del gas natural

La combinación del conocimiento humano con la capacidad computacional de los sistemas es uno de los pilares más sólidos y fiables en la gestión de activos actualmente. No cabe duda de que ya se puede vislumbrar un futuro en el que la digitalización va a ser una parte capital en la gestión de las empresas. De hecho, en las compañías que tienen muy definidos sus procesos productivos, lleva siendo una realidad desde hace tiempo. Sin embargo, las empresas de gestión de infraestructuras tienen una serie de retos que no permiten un avance tan claro en este campo. Primero, porque sus activos no responden de manera directa a unas variables claras que permitan tomar decisiones de manera automática. Y segundo, porque aunque se consiguiera definir este “algoritmo” de decisión, se necesitaría de una base sólida de información fiable que alimentara dicho sistema, premisa que a día de hoy está muy lejana. Por lo tanto, todas las aplicaciones digitales con una implantación real en la gestión de activos, tienen a día de hoy una componente de conocimiento especialista muy importante. Debido a esa carencia histórica de datos fiables que ahora se consideran tan necesarios y básicos para la creación de modelos y algoritmos, las soluciones que se están implantando tienden a partir de una base importante de conocimiento humano. Así que, si bien existe un futuro automatizado, no sólo en el proceso productivo, sino el ámbito de gestión, la clave estará en como diseñar el modelo que lo hará funcionar de una manera efectiva. Es justo en ese momento, donde las metodologías de gestión de mantenimiento propuestas en la tesis presentan una forma sencilla e intuitiva de hacer una primera aproximación a la gestión en base a analíticas avanzadas sin necesidad de ceder el control total a un algoritmo. El gran valor de estas técnicas, es ser capaz de transformar de una manera simple el inmenso conocimiento de los especialistas en un ámbito técnico concreto, a formulaciones con criterios objetivos de cálculo, que derivarán en indicadores o valores que ayudarán en la gestión. Por supuesto, estas técnicas también tienen sus detractores, pues incluir como parte esencial el factor humano, es también incluir un factor de subjetividad y de posibilidad de error, mayor que el que aparentemente podría llegar a dar un ordenador. Sin embargo, cualquier ámbito de gestión no puede estar exento del factor humano que de hecho es el mayor factor diferencial que podemos encontrarnos. De esa manera el reto es ser capaces de llevar a valores, fórmulas y datos, las opiniones, sensaciones y aprendizajes que han acumulado los especialistas en torno a los activos industriales que manejan. Debido a su sencilla implantación e interpretación, estas metodologías permiten traducir todo ese conocimiento, y facilitar enormemente la comunicación, entre los especialistas técnicos, los mandos intermedios y la alta dirección. Al tener, gracias a estas técnicas, una comunicación más rápida y un mensaje más comprensible, se consiguen estrategias de gestión más eficiente y con una visión mucho más integral. Es por tanto alcance de la tesis ser capaz de identificar e integrar dentro del modelo actual de gestión de la compañía, herramientas de gestión de mantenimiento que permitan optimizar el ciclo de vida del activo. En concreto se desarrollarán dos técnicas centradas en dos ámbitos de gestión de los activos de gran valor para la compañía; la gestión del riesgo que persigue caracterizar los equipos en función del impacto de un hipotético fallo funcional, y la gestión operativa, que persigue mejorar el rendimiento de dichos activos durante toda su vida útil.The combination of human knowledge with the computational capacity of systems is one of the most solid and reliable pillars in asset management today. There is no doubt that we assume a future in which digitalization will be a key part of company management. In fact, in companies with well-defined production processes, it has been a reality for some time now. However, asset management companies face a series of challenges that do not allow such a clear advance in this field. Firstly, their assets do not respond directly to clear variables that allow automatic decision making. And secondly, even if this decision "algorithm" could be defined, it would require a solid base of reliable information to feed the system, a premise that is still far away. Therefore, all digital applications with a real implementation in asset management today, have a very important component of specialist knowledge. Due to this historical lack of reliable data that is now considered so necessary and basic for the creation of models and algorithms, the solutions that are being implemented tend to start from a significant base of human knowledge. In other words, if there is an automated future, not only in the production process, but also in the management area, the key will be how to design the model that will make it work in an effective way. It is precisely at this point where the maintenance management methodologies proposed in this thesis, present a simple and intuitive way to make a first approach to management based on advanced analytics without the need to let the control to an algorithm. The great value of these techniques is that allow to transform in a simple way the immense knowledge of specialists in a specific technical field, to formulations with objective calculation criteria, which will derive in indicators or values that will help in management. Of course, these techniques also have their detractors. To include the human factor as an essential part of the methodology is also to include a factor of subjectivity and the possibility of error. However, any field of management cannot be exempt from the human factor, which is in fact the greatest differential factor that we can find. Thus, the challenge is to be able to transform into values, formulas and data, the opinions, sensations and learning that specialists have accumulated around the industrial assets they manage. Due to their simple implementation and interpretation, these methodologies make it possible to digitalise all this knowledge and facilitate communication between technical specialists, middle management and top management. By having, thanks to these techniques, a faster communication and a more understandable message, more efficient management strategies with a much more comprehensive vision are achieved. It is the scope of the thesis to be able to identify and integrate within the current management model of the company, maintenance management tools to optimize the life cycle of the asset. Specifically, two techniques will be developed focused on two areas of asset management of great value for the company; risk management, which aims to characterize the equipment according to the impact of a hypothetical functional failure, and operational management, which aims to improve the performance of these assets throughout their useful life

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen

Aggregation of Health Assessment Indicators of Industrial Systems

International audienceIn the field of Prognostics and Health Management (P.H.M.), Health Monitoring can be seen as the first step to manage the global health state of complex systems. Health Monitoring of industrial systems focuses on accurately describing the health state of a system, using several equipment indicators. However, managers and maintainers have to make decisions. Such decisions can be hard to make while watching all indicators of the system simultaneously. In order to ease the decision making process, a synthetic indicator, which represent the actual system's state, can be used. In this paper, we will present an approach for building an aggregated indicator characterizing the global health state of a system. This approach was implemented on the TELMA platform (integrated TELeMAintenance platform for research and education) which simulates an industrial process