Wind turbine blade defect detection using hyperspectral imaging

Rizk, Patrick; Younès, Rafic; Ilinca, Adrian; Khoder, Jihan

doi:10.1016/j.rsase.2021.100522

Cited by 19 publications

(13 citation statements)

References 42 publications

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“…It can be an additional sensor only for fault diagnosis or a sensor already present in the system used by the control algorithms. The electromechanical machine or power system can be investigated by many different sensors and signals: current [ 71 , 72 ] and voltage [ 73 , 74 ], torque [ 75 , 76 ], angular velocity/position [ 77 , 78 ], linear 3-axis acceleration/speed/position [ 16 , 17 ], Doppler laser vibrometer [ 79 ], transmission coefficient and reflexion coefficient of omnidirectional antenna [ 80 ], strain/tension [ 81 , 82 , 83 , 84 ], power consumption [ 85 , 86 , 87 , 88 ], internal/external temperature at selected points [ 89 , 90 ] or surface temperature by thermal camera [ 91 , 92 ], depending on frequency range: displacement [ 93 ], vibrations [ 15 , 18 , 94 , 95 , 96 ], sound [ 97 , 98 , 99 ], sound from several microphones [ 100 ] or ultrasound [ 101 , 102 ], vibro-acoustic [ 103 ], chemical analysis of lubrication [ 104 , 105 ], chemical analysis by spectral imaging [ 106 , 107 , 108 , 109 ], camera imaging in human colour spectrum [ 110 , 111 , 112 , <...…”

Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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The human-centric and resilient European industry called Industry 5.0 requires a long lifetime of machines to reduce electronic waste. The appropriate way to handle this problem is to apply a diagnostic system capable of remotely detecting, isolating, and identifying faults. The authors present usage of HTTP/1.1 protocol for batch processing as a fault diagnosis server. Data are sent by microcontroller HTTP client in JSON format to the diagnosis server. Moreover, the MQTT protocol was used for stream (micro batch) processing from microcontroller client to two fault diagnosis clients. The first fault diagnosis MQTT client uses only frequency data for evaluation. The authors’ enhancement to standard fast Fourier transform (FFT) was their usage of sliding discrete Fourier transform (rSDFT, mSDFT, gSDFT, and oSDFT) which allows recursively updating the spectrum based on a new sample in the time domain and previous results in the frequency domain. This approach allows to reduce the computational cost. The second approach of the MQTT client for fault diagnosis uses short-time Fourier transform (STFT) to transform IMU 6 DOF sensor data into six spectrograms that are combined into an RGB image. All three-axis accelerometer and three-axis gyroscope data are used to obtain a time-frequency RGB image. The diagnosis of the machine is performed by a trained convolutional neural network suitable for RGB image recognition. Prediction result is returned as a JSON object with predicted state and probability of each state. For HTTP, the fault diagnosis result is sent in response, and for MQTT, it is send to prediction topic. Both protocols and both proposed approaches are suitable for fault diagnosis based on the mechanical vibration of the rotary machine and were tested in demonstration.

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Section: General Structure Of Fault Diagnosis and Perspective Mainten...mentioning

confidence: 99%

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Łuczak

Brock

Siembab

2023

Sensors

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“…where R t is RFS of feature t, er 1 (i) is the OOB error in tree i, er 2t (i) is the second OOB error of feature t in tree i, P is the number of trees. The features are evaluated through the total score TS after weighting LS and RFS with normalized, as expressed in equation (7). The larger TS, the more important feature.…”

Section: Hybridized Feature Selection Using Laplacian Random Forest S...mentioning

confidence: 99%

“…[5][6] For image detection, the principle is to characterize damage propagation by image processing. 7 Fotouhi et al 8 proposed the crack recognition and quantitative assessment method for aircraft and wind turbine blades by AlexNet with high accuracy. Jac et al 9 classified the damages from the images of composite material based on geometrical features and Zernike moments.…”

Section: Introductionmentioning

confidence: 99%

Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Song

2022

Structural Health Monitoring

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Nowadays, acoustic emission (AE) has been widely used to the nondestructive evaluation (NDE) and structural health monitoring (SHM) for compressor blades. However, traditional AE features and feature selection methods are generally difficult to identify the cracked blades of compressor due to its complex structure and background noise. To solve this problem, the crack damage monitoring method based on novel feature and hybridized feature selection is proposed to identify crack of compressor blades, which is aimed at improving the crack identification accuracy with optimal features. First, the novel feature of spectral centroid with energy shift (SCES) is established. Besides, the hybridized feature selection method is proposed based on Laplacian random forest scores (LRFS), which can evaluate and select features adaptively. By fusing information of selected features from AE sensors, the long short-term memory (LSTM) network is used to classify cracked blades. The proposed method is applied experimentally to identify cracks at different speeds and locations of AE sensors, which has the average accuracy of 98.93%. The comparative results demonstrate the effectiveness and superiority of the proposed method in AE-based SHM for compressor blades under different working conditions.

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“…[1] Changes in the reflected or transmitted light spectrum help identify differences in the sample's chemical composition [2], the concentration of compounds [3] or even defects resulting in structural changes. [4] While many of these can also be identified in the visible spectrum (VIS), spectroscopy greatly benefits from studying spectral regions in other areasultraviolet and infrared. [5]- [10] Different or broader studied ranges can, for example, be used in biological studies as most tissues have a transparent window in the near-infrared (NIR) region.…”

Section: Introductionmentioning

confidence: 99%

Spatially Encoded Hyperspectral Compressive Microscope for Ultrabroadband VIS/NIR Hyperspectral Imaging

Klein¹,

Žídek²,

Touš³

2023

Preprint

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Hyperspectral imaging (HSI) has become a valuable tool in sample characterization in various scientific fields. While many approaches have been tested, specific applications and technology usually lead to only a narrow part of the spectrum being studied. We demonstrate the use of a broadband HSI setup based on compressed sensing capable of capturing data in visible (VIS), near-infrared (NIR), and short-wave infrared (SWIR) spectral regions. Using a tested design, we developed a dual configuration and tested its performance on a set of samples demonstrating spatial resolution and spectral reconstruction. Samples showing a potential use of the setup in optical defect detection are also tested. The setup showcases a dual single-pixel camera configuration capable of combining various detectors with a shared spatial modulation, further improving data efficiency and providing an affordable instrument from broadband spectral studies.

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Wind turbine blade defect detection using hyperspectral imaging

Cited by 19 publications

References 42 publications

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Cloud Based Fault Diagnosis by Convolutional Neural Network as Time–Frequency RGB Image Recognition of Industrial Machine Vibration with Internet of Things Connectivity

Crack damage monitoring for compressor blades based on acoustic emission with novel feature and hybridized feature selection

Spatially Encoded Hyperspectral Compressive Microscope for Ultrabroadband VIS/NIR Hyperspectral Imaging

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