P Moreu’s research while affiliated with University of Córdoba and other places

The chapter explains in detail the maintenance management model (MMM) taken as a reference for the development of the book. The chapter is based on the eight phases of the MMM. The first three blocks determine the effectiveness of the management; the following blocks assure the same efficiency and continuous improvement in the following way: Blocks 4 and 5 include actions for the planning and scheduling of maintenance, including, of course, the capacity of planning of department of maintenance. Blocks 6 and 7 are dedicated to the evaluation and control of the maintenance and the cost of assets throughout their life cycle. This chapter of introduction briefly summarizes the process and the reference frame necessary for the implementation of the MMM. This chapter also presents the relationship between the eight phases of the maintenance management model proposed and the general requirements of the asset management standard ISO 55000 to show how the gradual implementation of the MMM largely covers the requirements of the standard ISO 55000.

In the process of characterizing a given population collecting samples, there are machine learning applications today that provide a wide range of possibilities regarding, e.g., clustering and data mining topics. These possibilities consist of industrial and scientific application techniques that are adapted to each particular field for the successful achievement of results. As a fundamental element in statistical learning, this paper aims to understand in a simple way the use of the t-Student statistical distribution, clarifying the concepts of sampling error and convergence criterion based on an iterative process for the calculation of the optimal number of samples. With this reasoning and inference application of the t-Student distribution, this paper is intended to find the convenience of a procedure that can be used to discard or not sampling protocols, serving as a starting point till more reliable data can be available. In other words, regarding problem-solving and planning issues, and at the beginning from a preliminary situation where simplifications are made, it is intended here to estimate the distortions introduced by the measurements, so that according to different values of sampling error, a reasonable number of samples can be obtained. As a criterion of convergence of the algorithm for calculating the number of samples, the objective here will be to determine a minimum number of characterizations that will reduce costs and efforts, while adjusting to the desired confidence level considering the error of the measurements. With this purpose, this chapter begins introducing concepts from probabilistic models and methods, in order to propose after that a sampling mathematical protocol. Then, the new protocol is validated by simulation in some study cases. Finally, this chapter ends discussing possible future researches on this field and with some conclusions.

This paper deals with the process of criticality analysis in overhead power lines, as a tool to improve maintenance, felling & pruning programs. Felling & pruning activities are tasks that utility companies must accomplish to respect the servitudes of the overhead lines, concerned with distances to vegetation, buildings, infrastructures and other networks crossings. Conceptually, these power lines servitudes can be considered as failure modes of the maintainable items under our analysis (power line spans), and the criticality analysis methodology developed, will therefore help to optimize actions to avoid these as other failure modes of the line maintainable items. The approach is interesting, but another relevant contribution of the paper is the process followed for the automation of the analysis. Automation is possible by utilizing existing companies IT systems and databases. The paper explains how to use data located in Enterprise Assets Management Systems, GIS and Dispatching systems for a fast, reliable, objective and dynamic criticality analysis. Promising results are included and also discussions about how this technique may result in important implications for this type of businesses.

Resumen El objetivo de este artículo es hacer una revisión de distintas herramientas software que dan soporte al análisis de la fiabilidad, disponibilidad, mantenibilidad y seguridad (RAMS) de equipos industriales. Para ello, se describen y enumeran las principales características modulares de hasta tres herramientas representativas del mercado. Se pretende, por un lado, dar a conocer las distintas estructuras operativas en las que se presentan este tipo de software y sus características y, por otro, facilitar la comparación entre todas ellas mediante un análisis tabular descriptivo de los módulos comunes y no comunes. Posteriormente, se proponen un conjunto de factores de caracterización que deben tenerse en cuenta a la hora de analizar detalladamente una herramienta RAMS, ya que pueden determinar en gran medida sus funcionalidades y su adecuación a las necesidades específicas en cada caso. 1. INTRODUCCION Una de las estrategias más modernas desarrolladas para gestionar el mantenimiento en todos sus aspectos es la basada en los análisis RAMS: Reliability, Availability, Maintainabilty & Safety, es decir, basada en los análisis de Fiabilidad, Disponibilidad, Mantenibilidad y Seguridad de los equipos, cuatro factores claves a tener en cuenta para realizar una óptima gestión del mantenimiento industrial tanto en la fase preparatoria de un equipo como en su fase de operación. Los análisis RAMS permiten pronosticar la indisponibilidad de un proceso o equipo, de acuerdo a su configuración, fiabilidad de sus componentes, políticas de mantenimiento, recursos disponibles y filosofía operacional. El análisis tiene en cuenta, entre otros factores: la fiabilidad de los equipos, la configuración del sistema, los fallos aleatorios y sus reparaciones, la influencia del "error humano", las pérdidas de capacidad por degradación, el tiempo fuera de servicio por mantenimiento planificado, la disponibilidad de recursos humanos y materiales y la probabilidad de ocurrencia de eventos especiales no deseados. Estos análisis permiten predecir la mayoría de los