scholarly journals Image Enhanced Mask R-CNN: A Deep Learning Pipeline with New Evaluation Measures for Wind Turbine Blade Defect Detection and Classification

2021 ◽  
Vol 7 (3) ◽  
pp. 46
Author(s):  
Jiajun Zhang ◽  
Georgina Cosma ◽  
Jason Watkins

Demand for wind power has grown, and this has increased wind turbine blade (WTB) inspections and defect repairs. This paper empirically investigates the performance of state-of-the-art deep learning algorithms, namely, YOLOv3, YOLOv4, and Mask R-CNN for detecting and classifying defects by type. The paper proposes new performance evaluation measures suitable for defect detection tasks, and these are: Prediction Box Accuracy, Recognition Rate, and False Label Rate. Experiments were carried out using a dataset, provided by the industrial partner, that contains images from WTB inspections. Three variations of the dataset were constructed using different image augmentation settings. Results of the experiments revealed that on average, across all proposed evaluation measures, Mask R-CNN outperformed all other algorithms when transformation-based augmentations (i.e., rotation and flipping) were applied. In particular, when using the best dataset, the mean Weighted Average (mWA) values (i.e., mWA is the average of the proposed measures) achieved were: Mask R-CNN: 86.74%, YOLOv3: 70.08%, and YOLOv4: 78.28%. The paper also proposes a new defect detection pipeline, called Image Enhanced Mask R-CNN (IE Mask R-CNN), that includes the best combination of image enhancement and augmentation techniques for pre-processing the dataset, and a Mask R-CNN model tuned for the task of WTB defect detection and classification.

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2764 ◽  
Author(s):  
Jianjun Chen ◽  
Weihao Hu ◽  
Di Cao ◽  
Bin Zhang ◽  
Qi Huang ◽  
...  

Wind power penetration has increased rapidly in recent years. In winter, the wind turbine blade imbalance fault caused by ice accretion increase the maintenance costs of wind farms. It is necessary to detect the fault before blade breakage occurs. Preliminary analysis of time series simulation data shows that it is difficult to detect the imbalance faults by traditional mathematical methods, as there is little difference between normal and fault conditions. A deep learning method for wind turbine blade imbalance fault detection and classification is proposed in this paper. A long short-term memory (LSTM) neural network model is built to extract the characteristics of the fault signal. The attention mechanism is built into the LSTM to increase its performance. The simulation results show that the proposed approach can detect the imbalance fault with an accuracy of over 98%, which proves the effectiveness of the proposed approach on wind turbine blade imbalance fault detection.


2019 ◽  
Vol 118 ◽  
pp. 02041 ◽  
Author(s):  
Ningning Zhang ◽  
Chengzhi Lu ◽  
Anmin Wang

Currently in the process of wind farm inspection, wind turbine blade appearance inspection mainly adopts the telescope or high-definition cameras, low detection efficiency, labor intensity and the precision is limited, in order to solve this problem, a kind of wind turbine blades defect recognition system based on image array is proposed. Through the joint of array camera and image processing server, the functions of the image acquisition, processing, and defect recognition and detection results output are implemented. The software of artificial intelligence deep learning based on neural network algorithm is used to identify the defects of blade image, and output quality analysis report, to realize automatic detection of wind turbine blade surface defect. The field measurement results show that the system greatly improves the efficiency and accuracy of wind turbine blade defect detection.


Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1452
Author(s):  
Liwei Deng ◽  
Yangang Guo ◽  
Borong Chai

Wind power generation is a widely used power generation technology. Among these, the wind turbine blade is an important part of a wind turbine. If the wind turbine blade is damaged, it will cause serious consequences. The traditional methods of defect detection for wind turbine blades are mainly manual detection and acoustic nondestructive detection, which are unsafe and time-consuming, and have low accuracy. In order to detect the defects on wind turbine blades more safely, conveniently, and accurately, this paper studied a defect detection method for wind turbine blades based on digital image processing. Because the log-Gabor filter used needed to extract features through multiple filter templates, the number of output images was large. Firstly, this paper used the Lévy flight strategy to improve the PSO algorithm to create the LPSO algorithm. The improved LPSO algorithm could successfully solve the PSO algorithm’s problem of falling into the local optimal solution. Then, the LPSO algorithm and log-Gabor filter were used to generate an adaptive filter, which could directly output the optimal results in multiple feature extraction images. Finally, a classifier based on HOG + SVM was used to identify and classify the defect types. The method extracted and identified the scratch-type, crack-type, sand-hole-type, and spot-type defects, and the recognition rate was more than 92%.


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