The generation of energy from renewable sources is subjected to very dynamic changes in environmental parameters and asset operating conditions. This is a very relevant issue to be considered when developing reliability studies, modeling asset degradation and projecting renewable energy production. To that end, Artificial Neural Network (ANN) models have proven to be a very interesting tool, and there are many relevant and interesting contributions using ANN models, with different purposes, but somehow related to real-time estimation of asset reliability and energy generation. This document provides a precise review of the literature related to the use of ANN when predicting behaviors in energy production for the referred renewable energy sources. Special attention is paid to describe the scope of the different case studies, the specific approaches that were used over time, and the main variables that were considered. Among all contributions, this paper highlights those incorporating intelligence to anticipate reliability problems and to develop ad-hoc advanced maintenance policies. The purpose is to offer the readers an overall picture per energy source, estimating the significance that this tool has achieved over the last years, and identifying the potential of these techniques for future dependability analysis.

Purpose – The purpose of this paper is to define a process for maintenance management and to classify maintenance engineering techniques within that process. Design/methodology/approach – The paper presents a generic model proposed for maintenance management which integrates other models found in the literature for built and in-use assets, and consists of eight sequential management building blocks. The different maintenance engineering techniques are playing a crucial role within each one of those eight management building blocks. Following this path it characterizes the “maintenance management framework”, i.e. the supporting structure of the management process. Findings – The paper offers a practical vision of the set of activities composing each management block, and the result of the paper is a classification of the different maintenance engineering tools. The discussion of the different tools can also classify them as qualitative or quantitative. At the same time, some tools will be very analytical tools while others will be highly empirical. The paper also discusses the proper use of each tool or technique according to the volume of data/information available. Practical implications – As a consequence, of the implementation of advanced manufacturing technologies and just-in-time production systems, the nature of the production environment has changed during the last two decades. This has allowed companies to massively produce products in a customized way. But the increase in automation and the reduction in buffers of inventory in the plants clearly put more pressure on the maintenance system. The present maintenance management framework has been proposed in order to diminish this pressure. Whatever the model an organization adopts, it has to be evolving to continue being useful against the fast changes that occur in business, communications and industry. A key to achieve this could be the incorporation of the techniques proposed in this paper besides the integration of platforms known as “next generation manufacturing practices” This implies the use of e-maintenance as a sub-concept of e-manufacturing and e-business. Originality/value – This paper presents not only a process but also the framework and techniques to manage and improve maintenance effectiveness and efficiency. This paper will be useful to researchers, maintenance professionals and others concerned with maintenance management.

Maintenance Management is a key pillar in companies, especially energy utilities, which have high investments in assets, and so for its proper contribution has to be integrated and aligned with other departments in order to conserve the asset value and guarantee the services. In this line, Intelligent Assets Management Platforms (IAMP) are defined as software platforms to collect and analyze data from industrial assets. They are based on the use of digital technologies in industry. Beside the fact that monitoring and managing assets over the internet is gaining ground, this paper states that the IAMPs should also support a much better balanced and more strategic view in existing asset management and concretely in maintenance management. The real transformation can be achieved if these platforms help to understand business priorities in work and investments. In this paper, we first discuss about the factors explaining IAMP growth, then we explain the importance of considering, well in advance, those managerial aspects of the problem, for proper investments and suitable digital transformation through the adoption and use of IAMPs. A case study in the energy sector is presented to map, or to identify, those platform modules and Apps providing important value-added features to existing asset management practices. Later, attention is paid to the methodology used to develop the Apps’ data models from a maintenance point of view. To illustrate this point, a methodology for the development of the asset criticality analysis process data model is proposed. Finally, the paper includes conclusions of the work and relevant literature to this research.