Structural Health Monitoring for Jacket-Type Offshore Wind Turbines: Experimental Proof of Concept

Structural health monitoring of jacket-type support structures in offshore wind turbines: A comprehensive dataset for bolt loosening detection through vibrational analysis

This dataset provides a comprehensive collection of vibrational data for the purpose of structural health monitoring, particularly focusing on the detection of bolt loosening in offshore wind turbine jacket foundations. The data set comprises 780 comma-separated values (CSV) files, each corresponding to specific experimental conditions, including various structural states of the wind turbine's support structure. These states are systematically varied considering three main aspects: the amplitude of a white noise (WN) signal, the type of bolt damage, and the level at which damage has occurred. The data were meticulously collected using eight triaxial accelerometers (PCB R Piezotronic model 356A17), strategically placed at different locations on a scaled-down replica of an offshore jacket-type wind turbine. This setup facilitated the acquisition of detailed vibrational data through a National Instruments' data acquisition (DAQ) system, comprising six input modules (NI 9234 model) housed in a chassis (cDAQ model). The white noise signal, simulating wind disturbance at the nacelle, was produced by a modal shaker and varied in three amplitudes (0.5, 1, and 2), directly proportional to the induced vibration in the wind turbine. The dataset uniquely captures the vibrational behaviour under different scenarios of bolt loosening in the turbine's foundation. The conditions include a healthy state (bolts tightened to 12 Nm) and various degrees of loosening (bolts loosened to 9 Nm, 6 Nm, and completely absent), examined at four distinct levels of the turbine's base structure. This granular approach offers a nuanced view of how varying degrees of bolt loosening impact the vibrational characteristics of the structure. The value of this dataset lies in its potential for wide-ranging applications in the field of structural health monitoring. Researchers and engineers can leverage this data for developing and testing new methodologies for early damage detection and progressive damage assessment in offshore wind turbines. The dataset's comprehensive coverage of damage scenarios makes it a valuable resource for the validation and enhancement of existing damage detection algorithms. Furthermore, the dataset can serve as a benchmark for comparing the efficacy of different vibrational analysis techniques in the context of wind turbine maintenance and safety. Its application is not only limited to wind turbines but can extend to other structures where bolt integrity is critical for operational safety. This dataset represents a significant contribution to the field of structural health monitoring, providing a detailed and practical resource for enhancing the reliability and safety of offshore wind turbines and similar structures.

Health-Monitoring Systems for Marine Structures: A Review

This paper presents a comprehensive review of the state-of-the-art developments in health monitoring of marine structures. Monitoring the health of marine structures plays a key role in reducing the risk of structural failure. The authors establish the different sensors with their theoretical foundations and applications in order to determine the optimal position of the sensors on board. Once the data were collected, it was necessary to use for subsequent treatment; thus, the authors identified the different methodologies related to the treatment of data collected by the sensors. The authors provide a historical review of the location of different sensors depending on the type of ship and offshore platform. Finally, this review paper states the conclusions and future trends of this technology.

Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

Advanced Development of Sensors’ Roles in Maritime-Based Industry and Research: From Field Monitoring to High-Risk Phenomenon Measurement

The development of human civilization over the last decade has reached a landmark as Industry 4.0 has been widely introduced. Several aspects of industry and manufacturing activities are changing due to the Internet of Things (IoT), location detection technologies, and advanced human–machine interfaces. To enact industrial affairs under those specifications, a sensor is required to transform physical events into numerical information. The use of sensors in marine applications also appears in research and studies, in which the sensor is used for both monitoring the phenomena of a designated subject and data acquisition. Achievements in quantifying complex phenomena in critical maritime designs are fascinating subjects to discuss regarding their development and current states, which may be reliable references for further research on developing sensors and related measurement analysis tools in marine, shipbuilding, and shipping fields. This comprehensive review covers several discussion topics, including the origins and development of sensor technology, applied sensor engineering in logistic and shipping activities, the hydrodynamic characterization of designed hulls, the monitoring of advanced machinery performance, Arctic-based field observations, the detection of vibration-based damage to offshore structures, corrosion control and monitoring, and the measurement of explosions on critical maritime infrastructures.

Structural Damage Classification in a Jacket-Type Wind-Turbine Foundation Using Principal Component Analysis and Extreme Gradient Boosting

Damage classification is an important topic in the development of structural health monitoring systems. When applied to wind-turbine foundations, it provides information about the state of the structure, helps in maintenance, and prevents catastrophic failures. A data-driven pattern-recognition methodology for structural damage classification was developed in this study. The proposed methodology involves several stages: (1) data acquisition, (2) data arrangement, (3) data normalization through the mean-centered unitary group-scaling method, (4) linear feature extraction, (5) classification using the extreme gradient boosting machine learning classifier, and (6) validation applying a 5-fold cross-validation technique. The linear feature extraction capabilities of principal component analysis are employed; the original data of 58,008 features is reduced to only 21 features. The methodology is validated with an experimental test performed in a small-scale wind-turbine foundation structure that simulates the perturbation effects caused by wind and marine waves by applying an unknown white noise signal excitation to the structure. A vibration-response methodology is selected for collecting accelerometer data from both the healthy structure and the structure subjected to four different damage scenarios. The datasets are satisfactorily classified, with performance measures over 99.9% after using the proposed damage classification methodology.

Damage Diagnosis for Offshore Wind Turbine Foundations Based on the Fractal Dimension

Cost-competitiveness of offshore wind depends heavily in its capacity to switch preventive maintenance to condition-based maintenance. That is, to monitor the actual condition of the wind turbine (WT) to decide when and which maintenance needs to be done. In particular, structural health monitoring (SHM) to monitor the foundation (support structure) condition is of utmost importance in offshore-fixed wind turbines. In this work a SHM strategy is presented to monitor online and during service a WT offshore jacket-type foundation. Standard SHM techniques, as guided waves with a known input excitation, cannot be used in a straightforward way in this particular application where unknown external perturbations as wind and waves are always present. To face this challenge, a vibration-response-only SHM strategy is proposed via machine learning methods. In this sense, the fractal dimension is proposed as a suitable feature to identify and classify different types of damage. The proposed proof-of-concept technique is validated in an experimental laboratory down-scaled jacket WT foundation undergoing different types of damage.

Vibration-Response-Only Structural Health Monitoring for Offshore Wind Turbine Jacket Foundations via Convolutional Neural Networks

This work deals with structural health monitoring for jacket-type foundations of offshore wind turbines. In particular, a vibration-response-only methodology is proposed based on accelerometer data and deep convolutional neural networks. The main contribution of this article is twofold: (i) a signal-to-image conversion of the accelerometer data into gray scale multichannel images with as many channels as the number of sensors in the condition monitoring system, and (ii) a data augmentation strategy to diminish the test set error of the deep convolutional neural network used to classify the images. The performance of the proposed method is analyzed using real measurements from a steel jacket-type offshore wind turbine laboratory experiment undergoing different damage scenarios. The results, with a classification accuracy over 99%, demonstrate that the stated methodology is promising to be utilized for damage detection and identification in jacket-type support structures.