An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation

Solimine, Jaclyn; İnalpolat, Murat

doi:10.1177/0309524x221080470

Cited by 4 publications

(3 citation statements)

References 56 publications

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“…The aim of feature extraction is to summarize the salient signal characteristics in each frame to optimize the input for machine learning. In this study, linear frequency cepstral coefficients (LFCC) were extracted largely due to their previous success in characterizing WTB damage (Solimine and Inalpolat, 2022). LFCC’s are derived from cepstral coefficients, which are calculated from the inverse Fourier transform of the logarithm of a spectrum (equation (1) (Randall, 2011)),…”

Section: Methodsmentioning

confidence: 99%

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

Solimine

İnalpolat

2023

Wind Engineering

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This paper proposes a new in-situ damage detection approach for wind turbine blades, which leverages blade-internal non-stationary acoustic pressure fluctuations caused by the mechanical loading as the main source of excitation. This acoustic excitation was leveraged for the detection of fatigue-related damage modes on a full-scale wind turbine blade undergoing edgewise fatigue testing. An unsupervised, data-driven structural health monitoring strategy was developed to learn the normal cavity-internal acoustic sequences generated by the blade’s load cycles and to detect damage-related anomalies in the context of those sequences. A linear cepstral-coefficient based feature set was used to characterize the cavity-internal acoustics and LSTM-autoencoders were trained to accurately reconstruct healthy-case sequences. The reconstruction error was then used to characterize anomalous acoustic patterns within the blade cavity. The technique was able to detect a damage event earlier than a strain-based system by 120,000 load cycles.

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Section: Methodsmentioning

confidence: 99%

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

Solimine

İnalpolat

2023

Wind Engineering

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“…The experimental results show that the 1, 2, 4, 6, and 8 dimensions of the 12-dimensional MFCC features were more obvious in distinguishing the faulty samples from the normal ones. Solimine and Inalpolat [189] set up simulated damage in the form of penetrating holes and cracks at three different locations (the leading edge of the front cavity, the side of the front cavity, and the side of the back cavity) and compared the MFCC, GTCC, LFCC40, and LFCC80 feature sets, finding that the LFCC40 and LFCC80 feature sets provided the highest detection accuracy in both hole-type and crack-damage-type damage and significantly outperformed the common MFCC feature set. If a damage location is given, a hole-type damage of 0.16 cm can be detected 100% of the time, and the accuracy of detecting cracks with a minimum length of 1.27 cm can reach 97%.…”

Section: Aerodynamic Noise Feature Extractionmentioning

confidence: 99%

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Ding

Chao

Zhang

2023

Sensors

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Monitoring and maintaining the health of wind turbine blades has long been one of the challenges facing the global wind energy industry. Detecting damage to a wind turbine blade is important for planning blade repair, avoiding aggravated blade damage, and extending the sustainability of blade operation. This paper firstly introduces the existing wind turbine blade detection methods and reviews the research progress and trends of monitoring of wind turbine composite blades based on acoustic signals. Compared with other blade damage detection technologies, acoustic emission (AE) signal detection technology has the advantage of time lead. It presents the potential to detect leaf damage by detecting the presence of cracks and growth failures and can also be used to determine the location of leaf damage sources. The detection technology based on the blade aerodynamic noise signal has the potential of blade damage detection, as well as the advantages of convenient sensor installation and real-time and remote signal acquisition. Therefore, this paper focuses on the review and analysis of wind power blade structural integrity detection and damage source location technology based on acoustic signals, as well as the automatic detection and classification method of wind power blade failure mechanisms combined with machine learning algorithm. In addition to providing a reference for understanding wind power health detection methods based on AE signals and aerodynamic noise signals, this paper also points out the development trend and prospects of blade damage detection technology. It has important reference value for the practical application of non-destructive, remote, and real-time monitoring of wind power blades.

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“…The detection method of acoustic emission can obtain the defects and damage that the composite material has, and in its development, because of the rich and active amount of information, this technique can be applied to more advanced production and research and development, playing a very important role, but it also has certain disadvantages, making it difficult to distinguish between signal and noise [11]. Existing passive acoustics-based techniques for wind turbine blade damage detection lack the robustness and adaptability necessary for an operational implementation due to their physics-and model-based dependency [12].…”

Section: Introductionmentioning

confidence: 99%

Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

Guan,

Mu,

Yin

et al. 2024

EAI Endorsed Trans Energy Web

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INTRODUCTION: As a renewable and clean use of energy, wind power generation has a very important role in the new energy generation industry. For the many parts of various wind turbines, the safety and reliability of wind turbine blades are very important. OBJECTIVES: The energy spectrum simulation algorithm included in the wavelet analysis method is used to simulate and analyzewind turbine blade damage, to verify the correctness and validity of wind turbine blade damage analysis. METHODS: Matlab simulation is used to introduce the experiments related to the static and dynamic detection of fiber grating sensors, analyze the signal characteristics of the wind turbine blade when it is damaged by the impact, and provide a basis for the analysis of the external damage of large wind turbine blade. RESULTS: The main results obtained in this paper are the following. By analyzing the decomposition of wavelet packets, the gradient change of wavelet impact energy spectrum before and after the wavelet damage was obtained and compared with the histogram, and the impact energy spectrum of each three-dimensional wavelet energy packet in the image was compared and analyzed, which can well realize the recognition of wavelet damage gradient for solid composite materials. CONCLUSION: With the help of Matlab simulation to collect the impact response signal, using the wavelet packet energy spectrum method to analyze the signal, can derive the characteristics of wind turbine blade damage.

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An unsupervised data-driven approach for wind turbine blade damage detection under passive acoustics-based excitation

Cited by 4 publications

References 56 publications

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

Unsupervised acoustic detection of fatigue-induced damage modes from wind turbine blades

Acoustic-Signal-Based Damage Detection of Wind Turbine Blades—A Review

Characterization and Prediction of Wind Turbine Blade Damage Based on Fiber Grating Sensor

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