The sustainability of the global energy production systems involves new renewable energies and the improvement of the existing ones. Photovoltaic industry is growing thanks to the development of new technologies that increase the performance of photovoltaic systems. These systems are commonly subject to harsh environmental conditions that decrease their energy production and efficiency. In addition, current photovoltaic technologies are more sophisticated, and the size of photovoltaics solar plants is growing. Under this framework, research on failures and degradation mechanisms, together with the improvement of maintenance management, becomes essential to increase the performance, efficiency, reliability, availability, safety, and profitability of these systems. To assess maintenance needs, this paper presents a double contribution: an exhaustive literature review and updated survey on maintenance of photovoltaic plants, and a novel analysis of the current state and a discussion of the future trends and challenges in this field. An analysis of the main faults and degradation mechanisms is done, including the causes, effects, and the main techniques to detect, prevent and mitigate them.

The global energy production model is changing from fossil fuels to renewable and nuclear energies. Concentrated solar power is one of the growing technologies that is leading this process. This growth implies the sophistication and size of the systems and, therefore, it requires an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. The aim of this paper is to describe the current context of concentrated solar power, to summarise and analyse the main degradation mechanisms and the main techniques to detect, prevent and mitigate these faults. An exhaustive literature study is presented, considering the most advanced techniques and approaches. A novel qualitative and quantitative analysis of the literature is provided. Finally, the current trends and the future challenges in this field are gathered from this study.

Support of artificial intelligence, renewable energy and sustainability is currently increasing through the main policies of developed countries, e.g., the White Paper of the European Union. Wind energy is one of the most important renewable sources, growing in both onshore and offshore types. This paper studies the most remarkable artificial intelligence techniques employed in wind turbines monitoring systems. The principal techniques are analysed individually and together: Artificial Neural Networks; Fuzzy Logic; Genetic Algorithms; Particle Swarm Optimization; Decision Making Techniques; and Statistical Methods. The main applications for wind turbines maintenance management are also analysed, e.g., economic, farm location, non-destructive testing, environmental conditions, schedules, operator decisions, power production, remaining useful life, etc. Finally, the paper discusses the main findings of the literature in the conclusions.

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In railways, using a track-and ride-quality monitoring system on inservice train has become desirable for coordination and security. Identification of the track-or train-related rough rides via train crew can be estimated to the nearest kilometre. However, if the train is equipped with a monitoring system a better location and track quality evaluation can be provided. These systems commonly use information such as GNSS and/or an odometer to provide location information.This work proposes a practical method for track alignment estimation using real data from an in-cab inertial measurement system and using also a novel method based on crosslevel variations. The speed estimation is done through speed-related harmonics detected on inertial sensors, which depend on speed and track characteristics; and distance correction is provided by comparing crosslevel derived from inertial sensors and a reference track geometry. The effectiveness and accuracy of the method is demonstrated with data collected between London and Ashford.

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Railway condition monitoring is the compound of activities aimed at the detection and mitigation of faults in both tracks and vehicles. This minimizes damage and disruption, and increases the usability and benefits of railways. Trackside monitoring provides frequent data, but is costly and ineffective for large segments of track. Onboard monitoring using dedicated vehicles provides deep knowledge of the track, but is infrequent and requires allocated resources and track availability. The use of sensors onboard passenger trains overcomes these issues by acquiring signals more frequently and with relatively inexpensive devices, hence the increasing use of these devices. This review presents the current techniques applied for track and train condition monitoring techniques using onboard sensors, as well as the use of trackside sensors.