Ainhoa Cortés

Pitting corrosion is a localized degradation mechanism that can significantly affect the structural integrity of steel components while remaining difficult to detect and characterize using conventional monitoring approaches. This work investigates the use of pulse-echo ultrasound and data-driven signal analysis for the observability and characterization of localized pitting in low-carbon steel. The ultrasonic response of two 20 MHz single-element contact transducers was investigated through laboratory measurements on artificial pits and physics-based k-Wave simulations spanning a wider range of pit sizes and morphologies. The results show that localized pits modify the received signal through attenuation, scattering, waveform distortion, and the generation of secondary reflections, with the observed ultrasonic response strongly dependent on pit morphology and transducer aperture. A signal-processing and feature-extraction framework was developed to analyze these changes and to evaluate pit observability prior to characterization. Using pit-free measurements, observability limits were quantified with an autoencoder trained on signal descriptors, demonstrating that pit observability is influenced predominantly by pit geometry, normalized pit width, and pit volume rather than by depth alone. For signals exhibiting sufficiently observable and specular pit reflections, time-of-flight measurements enabled pit-depth estimation for defects deeper than 300 µm. To account for propagation-path distortions affecting pit-related echoes, an empirically derived cross-path delay compensation approach was developed using Random Forest models trained on synthetic signals, reducing systematic depth-estimation errors in the synthetic dataset and providing a physics-informed framework for future quantitative characterization studies. Additional Random Forest models employing waveform descriptors and reflection-amplitude indicators were used to assess pit morphology and acoustic surface specularity. The proposed framework was evaluated using laboratory measurements and through preliminary long-term monitoring of two pre-pitted specimens deployed in the HarshLab offshore environment. In these deployments, the methodology enabled simultaneous tracking of localized pit-wall evolution and overall backwall thickness changes from the same pulse-echo measurements, illustrating the potential of the approach for monitoring localized corrosion activity alongside generalized specimen wall loss.

Due to the growth of renewable energies, which requires cost reduction and efficiency in terms of structural health assessment, failure prevention, effective maintenance scheduling, and equipment lifespan optimization, in this paper, we propose an Ultra Wideband (UWB)-based accelerometer Sensor Node for low-power applications in offshore platforms. The proposed Sensor Node integrates a high-resolution accelerometer together with an Impulse Radio Ultra-Wideband (IR-UWB) transceiver. This approach enables effective remote monitoring of structural vibrations. This provides an easy-to-install, scalable, and flexible wireless solution without sacrificing robustness and low power consumption in marine environments. Additionally, due to the diverse and highly demanding applications of condition monitoring systems, we propose two modes of operation for the Sensor Node. It can be remotely configured to either transmit raw data for further analysis or process data at the edge. A hardware (HW) description of the proposed Sensor Node is provided. Moreover, we describe the power management strategies implemented in our system at the firmware (FW) level. We show detailed power consumption measurements, including power profiles and the battery-powered autonomy of the proposed Sensor Node. We compare data from a wired acquisition system and the proposed wireless Sensor Node in a laboratory environment.The wired sensor integrated into this acquisition system, fully characterized and tested, is our golden reference. Thus, we validate our proposal. Furthermore, this research work is within the scope of the SUREWAVE Project and is conducted in collaboration with the MARIN Institute, where wave basin tests are carried out to evaluate the behavior of a Floating Photovoltaic (FPV) system. These tests have provided a valuable opportunity to assess the effectiveness of the proposed Sensor Node for offshore platforms and to compare its performance with a wired system.

The development of floating photovoltaic systems (FPV) for coastal and offshore locations requires a solid understanding of a design’s hydrodynamic performance through reliable methods. This study aims to extend insights into the hydrodynamic behavior of a superficial multi-body FPV system in mild and harsh wave conditions through basin tests at scale 1:10, with specific interest in the performance of hinges that interconnect the PV panels. Particular effort is put into correctly scaling the elasticity of the flexible hinges that interconnect the PV modules. Tests of a 5 × 3 FPV matrix are performed, with and without shelter, by external floating breakwater (FBW). The results show that the PV modules move horizontally in the same phase when the wave length exceeds the length of the FPV system, but shorter waves result in relative motions between modules and, for harsh seas, in hinge buckling. Relative motions suggest that axial loads are highest for the hinges that connect the center modules in the system and for normal wave incidence, while shear loads are highest on the outward hinges and for oblique incidence. The FBW reduces hinge loads as it attenuates the high-frequency wave energy that largely drives relative motions between PV modules.

The conventional way of studying corrosion in marine environments is by installing corrosion coupons. Instead, this paper presents an experimental field study using an unattended corrosion sensor developed on the basis of ultrasound (US) technology to assess the thickness loss caused by general atmospheric corrosion on land close to the sea (coastal region). The system described here uses FPGA, low-power microcontroller, analog front-end devices in the sensor node, and a Beaglebone black wireless board for posting data to a server. The overall system is small, operates at low power, and was deployed at Gran Canaria to detect the thickness loss of an S355 steel sample and consequently estimate the corrosion rate. This experiment aims to demonstrate the system’s viability in marine environments and its potential to monitor corrosion in offshore wind turbines. In a day, the system takes four sets of measurements in 6 hour intervals, and each set consists of 5 consecutive measurements. Over the course of 5 months, the proposed experiment allowed for us to continuously monitor the corrosion rate in an equivalent corrosion process to an average thickness loss rate of 0.134 mm/year.

In this paper, we propose the design of a low-power wireless sensor network architecture that enables robust communications inside offshore wind turbines. This research work is in the scope of the WATEREYE EU Project, where we have designed a corrosion monitoring solution to work unattended. The architecture is composed of several fixed sensor nodes, one mobile sensor node, several anchors and the WATEREYE Computer (WEC). Our approach is based on Impulse Radio Ultra wideband (IR-UWB) technology offering reliable and low-power communications in these harsh environments. On top of that, we propose a double star network using two UWB channels for the following purposes: one network for communications to send the sensor data and another one for ranging estimations to calculate the indoor positioning of the mobile sensor node. The power strategies applied to our system, at Hardware (HW) and Firmware (FW) levels, are described in detail. Furthermore, we present power consumption measurements obtaining the power profiles and the autonomy of the most important components of the proposed architecture supplied by battery. On the other hand, we describe the methodology to analyze the range, reliability and continuity of the two UWB links providing the packet loss and gaps as a function of distance. The proposed communications system has been validated in three different scenarios considering two of them very hostile environments. Furthermore, one of the scenarios is a real offshore wind turbine.

The ultrasound technique is a well-known non-destructive and efficient testing method for on-line corrosion monitoring. Wall thickness loss rate is the major parameter that defines the corrosion process in this approach. This paper presents a smart corrosion monitoring system for offshore wind turbines based on the ultrasound pulse-echo technique. The solution is first developed as an ultrasound testbed with the aim of upgrading it into a low-cost and low-power miniaturized system to be deployed inside offshore wind turbines. This paper discusses different important stages of the presented monitoring system as design methodology, the precision of the measurements, and system performance verification. The obtained results during the testing of a variety of samples show meaningful information about the thickness loss due to corrosion. Furthermore, the developed system allows us to measure the Time-of-Flight (ToF) with high precision on steel samples of different thicknesses and on coated steel samples using the offshore standard coating NORSOK 7A.

In this paper, we propose a flexible Fog Computing architecture in which the main features are that it allows us to select among two different communication links (WiFi and LoRa) on the fly and offers a low-power solution, thanks to the applied power management strategies at hardware and firmware level. The proposed Fog Computing architecture is formed by sensor nodes and an Internet of Things (IoT) gateway. In the case of LoRa, we have the choice of implementing the LoRaWAN and Application servers on the cloud or on the IoT gateway, avoiding, in this case, to send data to the Cloud. Additionally, we have presented an specific setup and methodology with the aim of measuring the sensor node’s power consumption and making sure there is a fair comparison between the different alternatives among the two selected wireless communication links by varying the duty cycle, the size of the payload, and the Spreading Factor (SF). This research work is in the scope of the STARPORTS Interconnecta Project, where we have deployed two sensor nodes in the offshore platform of PLOCAN, which communicate with the IoT gateway located in the PLOCAN premises. In this case, we have used LoRa communications due to the required large distance between the IoT gateway and the nodes in the offshore platform (in the range of kilometers). This deployment demonstrates that the proposed solution operates in a real environment and that it is a low-power and robust approach since it is sending data to the IoT gateway during more than one year and it continues working.

In this paper, we analyze the performance of a positioning system based on the fusion of Ultra-Wideband (UWB) ranging estimates together with odometry and inertial data from the vehicle. For carrying out this data fusion, an Extended Kalman Filter (EKF) has been used. Furthermore, a post-processing algorithm has been designed to remove the Non Line-Of-Sight (NLOS) UWB ranging estimates to further improve the accuracy of the proposed solution. This solution has been tested using both a simulated environment and a real environment. This research work is in the scope of the PRoPART European Project. The different real tests have been performed on the AstaZero proving ground using a Radio Control car (RC car) developed by RISE (Research Institutes of Sweden) as testing platform. Thus, a real time positioning solution has been achieved complying with the accuracy requirements for the PRoPART use case.