Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure

Spanos, Nikos A; Sakellariou, John S.; Fassois, Spilios D.

doi:10.1177/1475921719862487

Cited by 33 publications

(21 citation statements)

References 22 publications

(35 reference statements)

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“…Finally, note that the purpose of the paper is to verify that the conceived methodology has practical potential. The laboratory tower is a simplified model, but it is valid for this preliminary study because it is similar to the laboratory towers used, for example, in [9], where damage detection is accomplished (but not localization or identification) via damage indicators; in [15,16], where statistical time series are employed to detect damage; and in [17,18], where damage detection is accomplished through principal component analysis and support vector machines.…”

mentioning

confidence: 99%

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

Puruncajas

Vidal

Tutivén

2020

Sensors

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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.

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confidence: 99%

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

Puruncajas

Vidal

Tutivén

2020

Sensors

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“…Also, since often there can be responses of high order [21], i.e. close to 100 [20], similar case have also been included here.…”

Section: Performance On Systems Of Higher Ordermentioning

confidence: 99%

Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

Papacharalampopoulos

2020

Processes

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System identification has been a major advancement in the evolution of engineering. As it is by default the first step towards a significant set of adaptive control techniques, it is imperative for engineers to apply it in order to practice control. Given that system identification could be useful in creating a digital twin, this work focuses on the initial stage of the procedure by discussing simplistic system order identification. Through specific numerical examples, this study constitutes an investigation on the most "natural" method for estimating the order from responses in a convenient and seamless way in time-domain. The method itself, originally proposed by Ho and Kalman and utilizing linear algebra, is an intuitive tool retrieving information out of the data themselves. Finally, with the help of the limitations of the methods, the potential future outlook is discussed, under the prism of forming a digital twin.

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“…In this work, facing the challenge posed by the previous references, different damage types are taken into account and its classification is achieved in an experimental down-scaled jacket WT foundation. It should be noted that the experimental testbed is a reduced model but well-founded for this proof-of-concept work as it is comparable to that employed in the following works: (i) [17], where damage detection is achieved via damage indicators; (ii) [18], where damage detection is obtained via statistical time series analysis; (iii) [19], based on principal component analysis and support vector machines; and (iv) [20], where a deep learning approach based on convolutional neural networks is employed.…”

Section: Introductionmentioning

confidence: 99%

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

2020

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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.

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Vibration-response-only statistical time series structural health monitoring methods: A comprehensive assessment via a scale jacket structure

Cited by 33 publications

References 22 publications

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

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

Investigating Data-Driven Systems as Digital Twins: Numerical Behavior of Ho–Kalman Method for Order Estimation

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

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