7,689 research outputs found
WIND TURBINE TOWER DETECTION USING FEATURE DESCRIPTORS AND DEEP LEARNING
Wind Turbine Towers (WTTs) are the main structures of wind farms. They are costly devices that must be thoroughly inspected according to maintenance plans. Today, existence of machine vision techniques along with unmanned aerial vehicles (UAVs) enable fast, easy, and intelligent visual inspection of the structures. Our work is aimed towards developing a visionbased system to perform Nondestructive tests (NDTs) for wind turbines using UAVs. In order to navigate the flying machine toward the wind turbine tower and reliably land on it, the exact position of the wind turbine and its tower must be detected. We employ several strong computer vision approaches such as Scale-Invariant Feature Transform (SIFT), Speeded Up Robust Features (SURF), Features from Accelerated Segment Test (FAST), Brute-Force, Fast Library for Approximate Nearest Neighbors (FLANN) to detect the WTT. Then, in order to increase the reliability of the system, we apply the ResNet, MobileNet, ShuffleNet, EffNet, and SqueezeNet pre-trained classifiers in order to verify whether a detected object is indeed a turbine tower or not. This intelligent monitoring system has auto navigation ability and can be used for future goals including intelligent fault diagnosis and maintenance purposes. The simulation results show the accuracy of the proposed model are 89.4% in WTT detection and 97.74% in verification (classification) problems
DefectNET: multi-class fault detection on highly-imbalanced datasets
As a data-driven method, the performance of deep convolutional neural
networks (CNN) relies heavily on training data. The prediction results of
traditional networks give a bias toward larger classes, which tend to be the
background in the semantic segmentation task. This becomes a major problem for
fault detection, where the targets appear very small on the images and vary in
both types and sizes. In this paper we propose a new network architecture,
DefectNet, that offers multi-class (including but not limited to) defect
detection on highly-imbalanced datasets. DefectNet consists of two parallel
paths, which are a fully convolutional network and a dilated convolutional
network to detect large and small objects respectively. We propose a hybrid
loss maximising the usefulness of a dice loss and a cross entropy loss, and we
also employ the leaky rectified linear unit (ReLU) to deal with rare occurrence
of some targets in training batches. The prediction results show that our
DefectNet outperforms state-of-the-art networks for detecting multi-class
defects with the average accuracy improvement of approximately 10% on a wind
turbine
Remote data acquisition for condition monitoring of wind turbines
While the number of offshore wind turbines is growing and turbines getting bigger and more expensive, the need for good condition monitoring systems is rising. From the research it is clear that failures of the gearbox, and in particular the gearwheels and bearings of the gearbox, have been responsible for the most downtime of a wind turbine. Gearwheels and bearings are being simulated in a multi-sensor environment to observe the wear on the surface
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