Vibration‐based monitoring of a small‐scale wind turbine blade under varying climate conditions. Part I: An experimental benchmark

Ou, Yaowen; Tatsis, Konstantinos; Dertimanis, Vasilis K.; Spiridonakos, Minas D.; Chatzi, Eleni

doi:10.1002/stc.2660

Cited by 34 publications

(24 citation statements)

References 95 publications

(106 reference statements)

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“…This, nevertheless, requires a quite comprehensive set of signals acquired at different stress magnitude and nature or direction to train the model, and will be treated in future work, as well as assessment of the present model into more realistic setups. Such a complex stress monitoring tool would require a considerable amount of rather expensive experimental data, so other data-driven strategies which employ high-fidelity simulations to create the models may be used [76][77][78].…”

Section: Discussionmentioning

confidence: 99%

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

2021

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Background Due to the acoustoelastic effect, ultrasonic guided waves have been used to estimate mechanical stress in a cheap and nondestructive fashion. Machine learning has been applied to map complex waveforms to stress estimates, though important aspects to construct and deploy real-time monitoring systems, such as accuracy and hardware consumption, to date, have not been concomitantly explored. Objective The goal of the present paper is to propose a data modeling methodology that optimizes accuracy and computational implementation, towards devising the best practices for real-time ultrasonic-based stress estimation. Methods We evaluate shallow and deep learning models with dimensionality reduction, which are compared to the most recent work showing overall better results for the approach presented herein both in terms of accuracy and hardware resources consumption. We generated a dataset to evaluate the models on a test rig with a plate measuring (i) ultrasonic guided waves excited by broadband signals and (ii) different stress conditions. The models are created and tested using a Monte-Carlo hold-out procedure with a repeated k-fold randomized hyperparameter search to evaluate their robustness in different stress conditions. ResultsWe dramatically improve the current state of the art by proposing and evaluating a more efficient model construction procedure. Results show that by using shallow models and principal component analysis, we were able to improve the model performance in terms of predictive power and inference hardware consumption. Specifically, we obtained an accuracy improvement of about 10% and hardware consumption used for inference smaller in three orders of magnitude when compared to the state-of-the-art reported with deep neural network models. Conclusions Results herein depicted impact the way practitioners may proceed for building efficient embedded predictive models for stress estimation using guided waves, enabling more precise, decentralized, and real-time nondestructive monitoring.

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Section: Discussionmentioning

confidence: 99%

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

2021

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“…In the table, the nomenclature for categories and sub-categories of phases, components, and topics reflect the previously introduced legend. Bajaj_2022 [276] x p3 x Xu_2022 [277] x p4 x x Ye_2021a [244] x p3 x Ahmed_2021 [172] x x p2 x x Mufazzal_2021 [278] x p4 x Yang_2021 [110] x p2 x x x x x Moghadam_2021 [193] x p2 x x Saucedo-Dorantes_2021 [173] x p2 x x x x Espinoza-Sepulveda_2021 [279] x p4 x x Kalista_2021 [73] x p1 x x x Zhang_2021a [280] x p4 x x Zhang_2021b [267] x p3 x x Meng_2021 [59] x p1 x x Tiwari_2021 [95] x p1 x x Tatsis_2021 [281] x p4 x x Leaman_2021 [282] x p5 x x Ou_2021 [283] x p5 x x Espinoza_2021 [226] x x p3 x x Wang_2021 [233] x p3 x x x Goyal_2021 [60] x p1 x x Bai_2021a [203] x p2 x x x Yu_2021 [75] x p1 x x Papathanasopoulos_2021 [66] x p1 x x Sharma_2021 [25] x p1 x x x Rauber_2021 [219] x p3 x x Laval_2021 [76] x p1 x x x x Shao_2021 [158] x x p2 x x Rafiq_2021 [166] x p2 x x x x Zhao_2021 [77] x p1 x x Gómez_2021 [284] x p1 x x Jablon_2021 [197] x p2 x x Barusu_2021 [62] x p1 x x x x Hadroug_2021 [250] x p3 x Hou_2021 [35] x x p1 x x Yuan_2021 [285] x p5 x Ye_2021b [245] x p3 x x x Tingarikar_2021 [286] x p4 x Ribeiro_2021 [287] x p2 x Peng_2021 [288] x x p2 x x x Gu_2021 …”

Section: Appendix Amentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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“…More advanced machine and deep learning algorithms might be preferred to capture complex patter in the data [12]. If interested in identifying the location of the damage and assessing it (levels II and III, respectively, as defined in [24]), the models to deploy typically require a structure that accounts for inter-dependencies between the sensing points, as studied in [25], [26].…”

Section: Vibration-based Damage Detection Of Wind Turbines and Offsho...mentioning

confidence: 99%

A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

Cevasco

Tautz-Weinert

Richmond

et al. 2022

ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering

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Structural failures of offshore wind turbine substructures might be less likely than failures of other equipment of the wind turbine generator but pose a high risk due to the possibility of catastrophic consequences. Significant costs are linked to offshore operations like inspections and maintenance, thus remote monitoring shows promise for cost-efficient structural integrity management. This work is aimed to investigate the feasibility of the two-level detection, in terms of anomaly identification and localisation, in the jacket structure of an offshore wind turbine. A monitoring scheme is developed based on a database of modal properties of the structure for different scenarios. The method identifies the correct anomaly scenario based on three types of modal indicators, namely natural frequency, the modal assurance criterion between mode shapes, and the modal flexibility variation. The supervised Fisher's linear discriminant analysis is applied to transform the modal indicators to maximise the separability of anomaly scenarios. A Fuzzy clustering algorithm is trained to predict the membership of new data to the scenarios in the database. In a case study, extreme scour phenomena and jacket member integrity loss are simulated, together with variations of the structural dynamics for environmental and operating conditions. Cross-validation is used to select the best hyperparameters and the effectiveness of the clustering is validated with slight variations of the environmental conditions. The results prove that it is feasible to detect and localise the simulated scenarios via the global monitoring of an offshore wind jacket structure.

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Vibration‐based monitoring of a small‐scale wind turbine blade under varying climate conditions. Part I: An experimental benchmark

Cited by 34 publications

References 95 publications

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

Improved Stress Estimation with Machine Learning and Ultrasonic Guided Waves

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Damage Detection and Location Scheme for Offshore Wind Turbine Jacket Structures Based on Global Modal Properties

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