The versatility of piezoelectric sensors in measurement techniques and their performance in applications has given rise to an increased interest in their use for structural and manufacturing component monitoring. They enable wireless and sensor network solutions to be developed in order to directly integrate the sensors into machines, fixtures and tools. Piezoelectric sensors increasingly compete with strain-gauges due to their wide operational temperature range, load and strain sensing accuracy, low power consumption and low cost. This research sets out the use of piezoelectric sensors for real-time monitoring of mechanical strength in metallic structures in the ongoing operational control of machinery components. The behaviour of aluminium and steel structures under flexural strength was studied using piezoelectric sensors. Variations in structural behaviour and geometry were measured, and the load and μstrains during operational conditions were quantified in the time domain at a specific frequency. The lead zirconium titanate (PZT) sensors were able to distinguish between material types and thicknesses. Moreover, this work covers frequency selection and optimisation from 20 Hz to 300 kHz. Significant differences in terms of optimal operating frequencies and sensitivity were found in both structures. The influence of the PZT voltage applied was assessed to reduce power consumption without signal loss, and calibration to μstrains and loads was performed.

The present paper examines the use of optical fiber as a distributed temperature sensor to detect leaks in earth dams. This real-time data acquisition technique complements current auscultation systems and improves monitoring of these critical infrastructures. The aim of this paper is to provide a practical illustration of this novel and promising technique and, particularly, of the precautions to consider to correctly deploy the sensor elements. For this last purpose, this document describes the field tests carried out on the Yesa dam in February 2017 and propose best practices concerning the implementation of the system. Keywords: Dam auscultation; Distributed temperature sensor; DTS; leak detection; earth dams

El presente trabajo pretende analizar el uso de fibra óptica como sensor de temperatura distribuido para la detección de posibles fugas en presas de materiales sueltos. El uso de esta técnica permite complementar los sistemas de auscultación normalmente utilizados mejorando la monitorización de las zonas críticas de la infraestructura. Este tipo de sistema de monitorización en tiempo real ha sido implementado en dos casos reales, (presa de Soto-Terroba y recrecimiento de la presa de Yesa). Finalmente este trabajo quiere proporcionar una visión práctica sobre esta nueva técnica implementada y en particular sobre los criterios de selección de materiales y las precauciones necesarias a la hora del despliegue del elemento sensor.

Leaks in ponds are a problem due to the loss of water resources, although the problem is greater when the ponds store livestock or agricultural waste (slurry or wastewater), in which case there is a risk of hydrogeological contamination of the environment. The proposed leak detection system is based on distributed temperature sensing (DTS) with hybrid fiber optics using the Raman effect. Using active detection techniques, i.e., applying a specific amount of electrical power to the copper wires that form part of the hybrid cable, it is possible to increase the temperature along the fiber and measure the thermal increments along it, detecting and locating the point of leakage. To validate the system, a full-scale prototype reservoir (25 m × 10 m × 3.5 m) was built, equipped with mechanisms to simulate leaks under the impermeable sheet that retains the reservoir’s contents. For environmental reasons, the tests were carried out with clean water. The results of the leak simulation showed significant differences in temperature increases due to the electrical pulse in the areas affected by the simulated leak (1 °C increase) and the areas not affected (5 °C increase). This technology, which uses hybrid fiber optics and a low-cost sensor, can be applied not only to ponds, but also to other types of infrastructure that store or retain liquids, such as dams, where it has already been tested, to measure groundwater flow, etc.

The Overall Equipment Effectiveness (OEE) indicator is a widely used key performance indicator (KPI) in industrial environments to assess equipment efficiency through the combination of availability, performance, and quality metrics. However, its direct application to renewable energy systems presents significant limitations due to the stochastic nature of the energy resource and the operating characteristics of wind turbines. This paper proposes an adaptation of Overall Equipment Effectiveness (OEE) for the wind energy sector, referred to as Overall Equipment Effectiveness for Renewable Energy (OEE_RE), in which the traditional availability, performance, and quality factors are reformulated to reflect the operational reality of utility-scale wind turbines. The proposed methodology defines the OEE_RE indicators using variables directly available from standard wind farm Supervisory Control and Data Acquisition systems (SCADA), enabling practical implementation without additional instrumentation. Special attention is devoted to the calculation of the performance indicator, which incorporates manufacturer power curve normalization according to IEC 61400-12 recommendations, including air density correction and turbulence intensity compensation. The quality indicator is derived from turbine-level electrical parameters associated with power generation performance. The methodology is validated using operational measurements collected from seven wind turbines within a commercial wind farm. The results demonstrate the feasibility of the proposed approach and show that performance and quality are the dominant contributors to OEE_RE variability, while availability remains close to unity under normal operating conditions. The proposed framework provides a practical and interpretable KPI for monitoring wind turbine efficiency, identifying operational deviations, and supporting performance optimization in renewable energy assets.