Early warning of damaged wind turbine blades using spatial–temporal spectral analysis of acoustic emission signals

Pan, Xiaoyong; Liu, Zhongdi; Xu, Ran; luo, jiehong; Shen, Yining; Qiu, Jianjun; Qi, Liqiang; Chen, Linxin

doi:10.1016/j.jsv.2022.117209

Cited by 11 publications

(3 citation statements)

References 25 publications

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“…Therefore, the rise time can be relied upon to determine the generation and increase in matrix cracks. Blade holes are also a manifestation of crack expansion; Pan et al [93] proposed a new method using sparse Bayesian learning beamforming to suppress ambient acoustic interference. Numerical simulation results showed that the inherent frequency of the acoustic emission signal tends to decrease in wind turbine blades in the presence of holes.…”

Section: Wind Turbine Blade Damage Detectionmentioning

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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Section: Wind Turbine Blade Damage Detectionmentioning

confidence: 99%

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

Ding

Chao

Zhang

2023

Sensors

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“…5,6 Nondestructive testing technology based on vibration has been widely used. 7 The mechanical failure of the blade will also cause the modification of modal parameters, 8,9 mainly natural frequency and modal shapes, which can effectively reveal the damage of the structure. Ou et al 10 proposed the appropriate statistical-based methods to detect damage location using a set of accelerometers.…”

Section: Introductionmentioning

confidence: 99%

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Jiang

Xiang

2023

Structural Health Monitoring

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Renewable energy has increased in recent years with a consequential increase in equipment maintenance. Maintenance costs can be reduced by structural health monitoring techniques especially for wind turbine (WT) blade damages. However, the majority are not suitable for on-line measurements and quantitative detections. A quantitative damage detection method is developed to identify multiple damages in a WT blade under in-service operation conditions. Firstly, singular value decomposition is applied to reveal singular information in the operating deflection shape (ODS), which can be treated as damage locations. Secondly, whale optimization algorithm is utilized for a damage severity decision about the natural frequency database between damage severities and natural frequencies, which are constructed by finite element method (FEM) simulations on the detected damage locations in the WT blade. The procedure is applied to FEM numerical simulations of a single WT blade with two and three damages. By adding a certain noise to the simulation dataset, the robustness of the present method is validated. Furthermore, the laser scanning vibrometer is employed to test the ODS as well as natural frequencies of WT blades to testify the performance of the multiple damage detection method. Results show that the present method is effective for the detection of multi-damage in WT blades with a certain noise robustness.

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“…Liang and Zhou (Liang and Zhou, 2022) used hierarchical DL to identify and locate faults in roller bearings. Pan et al (Pan et al, 2022) used a spatial-temporal transformation to formulate an early warning system of wind turbines. Lee et al (Lee et al, 2021) used CNNs to evaluate motor imbalance under variable speed operations.…”

Section: Introductionmentioning

confidence: 99%

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

Nashed

Renno

Mohamed

2023

Journal of Vibration and Control

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We present a novel method for real-time fault classification using the time history of acoustic emissions (AEs) recorded from a lab-scale gas turbine operating under normal and faulty conditions across multiple turbine speeds. Time-frequency features are extracted using the continuous wavelet transform, and for each signal, the root mean square (RMS) and kurtosis are calculated. We employ a color mapping technique to combine the time-frequency and statistical features into a single red–green–blue (RGB) image. The red channel is mapped to the time-frequency data, whereas the green and blue channels are mapped to the RMS and kurtosis, respectively. Subsequently, a deep convolutional neural network is trained on the generated images to classify the gas turbine condition. We show that the proposed model can form an online monitoring system using AEs to classify multiple running conditions at various turbine speeds. The methodology not only achieves real-time classification of faults but also minimizes the human intervention in identifying these faults. The datasets and codes used in this paper will be openly available.

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Early warning of damaged wind turbine blades using spatial–temporal spectral analysis of acoustic emission signals

Cited by 11 publications

References 25 publications

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

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

Method using singular value decomposition and whale optimization algorithm to quantitatively detect multiple damages in turbine blades

Fault classification using convolutional neural networks and color channels for time-frequency analysis of acoustic emissions

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