Introductory Chapter: Prognostics - An Overview

Prognostics, in general, can be defined as “knowledge beforehand”. Prognostics is usually identified with medical issues. Nowadays, due to the new advances in technologies and information systems, prognostic is beginning to be employed in other fields, e.g. engineering, financial, business, et

Introductory Chapter: An Overview to Maintenance Management

The industry requires maintenance to ensure the correct operations of the engines, components, structures, etc. Any failure, that is, termination of the ability of an item to perform a required function, generates downtimes, costs, risks for the human labors, etc. The high competitiveness in the current industry does not lead these failures to the firms

Introductory Chapter: Introduction to Dependability Engineering

Cloud computing presents some challenges that are needed to be overcome, such as planning infrastructures that maintain availability when failure events and repair activities occu

PREFACE: Handbook of Research on Big Data Clustering and Machine Learning

Big Data and Management Science has been designed and done to synthesize the analytic principles with business practice and big data. Specifically, the book provides an interface between the main disciplines of engineering/technology and the organizational, administrative, and planning abilities of management. It is complementary to other sub-disciplines such as economics, finance, marketing, decision and risk analysis, etc. The Advances in Analytics in Big Data synthesizes the analytic principles with Big Data and provides an interface between the main disciplines of engineering/economics and the organizational, administrative, and planning abilities of management. It is also complementary to other disciplines such as finance, marketing, decision and risk analysis. In this book each chapter discusses different topics in Advances in Business Analytic

Desarrollo de nuevos algoritmos aplicados al mantenimiento predictivo óptimo centrado en la fiabilidad y la monitorización remota basada en la condición (RCM2)

Desde el origen de la política común del transporte ferroviario hasta nuestros días, dicho sector ha sufrido numerosos cambios y mejoras en todos los campos, como son el legislativo, técnico, etc. Actualmente, con la incorporación de la alta velocidad, la seguridad de la superestructura ferroviaria es uno de los factores más importantes. Esta depende de los elementos que la componen, en especial de los elementos críticos, siendo uno de los más relevantes el desvío ferroviario. El incremento de seguridad de estos elementos ha sido impulsado por el uso de las nuevas tecnologías, ya que ha fomentado el desarrollo de nuevos sistemas de mantenimiento, como el empleado en esta tesis (rcm2). Rcm2 se basa en las técnicas de mantenimiento centrado en la fiabilidad (reliability-centred maintenance, rcm1) y monitorización remota basada en la condición (remote condition monitoring, rcm2) Con el fin de detectar fallos empleando esta técnica, se ha desarrollado un modelo que consta de siguientes criterios: . Anomalías en la forma de la curva. . Posición del maximo de las curvas fuerza [n] vs. Tiempo [s]. . Relación entre áreas de la curva separadas por el maximo. En dicho modelo se ha empleado el filtro de kalman (fk) con el fin de poder mejorar su eficiencia. La mejora se debe fundamentalmente a que la aplicación de los criterios anteriores a las curvas limpias de ruido (filtradas con el fk), produce una mejora sustancial en la detección de fallos. Finalmente se propone un modelo más elaborado dentro del marco de modelos de componentes no observables (en forma de espacio de los estados) en el que la forma de las curvas de referencia libres de fallos se comparan con el resto de la información existente a medida que llegan al centro de control

Introductory Chapter: Introduction to Lean Manufacturing

Lean manufacturing, also called lean production, was originally created in Toyota after the Second World War in the reconstruction period. It is based on the idea of eliminating any waste in the industry, i.e., any activity or task that does not add value and requires resources. It is considered in any level of the industry, e.g., design, manufacturing, distribution, and customer service

Digital Filters for Maintenance Management

Faults in mechanisms must be detected quickly and reliably in order to avoid important losses. Detection systems should be developed to minimize maintenance costs and are generally based on consistent models, but as simple as possible. Also, the models for detecting faults must adapt to external and internal conditions to the mechanism. The present chapter deals with three particular maintenance algorithms for turnouts in railway infrastructure by means of discrete filters that comply with these general objectives. All of them have the virtue of being developed within a well-known and common framework, namely the State Space with the help of the Kalman Filter (KF) and/or complementary Fixed Interval Smoother (FIS) algorithms. The algorithms are tested on real applications and thorough results are shown

Artificial Intelligence in Marine Science and Engineering

This Special Issue covers research in Artificial Intelligence in Marine Science and Engineering and shows how to apply it to many different professional areas, e [...