مقاله انگلیسی رایگان در مورد تشخیص مکان و خطای مولفه پشتیبانی کانکتور – IEEE 2018

 

مشخصات مقاله
انتشار مقاله سال 2018
تعداد صفحات مقاله انگلیسی 6 صفحه
هزینه دانلود مقاله انگلیسی رایگان میباشد.
منتشر شده در نشریه IEEE
نوع مقاله ISI
عنوان انگلیسی مقاله Location and Fault Detection of Catenary Support Components Based on Deep Learning
ترجمه عنوان مقاله تشخیص مکان و خطای مولفه های پشتیبانی کانکتور بر اساس یادگیری عمیق
فرمت مقاله انگلیسی  PDF
رشته های مرتبط مهندسی برق، مهندسی کامپیوتر
گرایش های مرتبط مهندسی کنترل، مکاترونیک، مهندسی الکترونیک، هوش مصنوعی
مجله کنفرانس بین المللی فناوری اندازه گیری و ابزار – International Instrumentation and Measurement Technology Conference
کلمات کلیدی راه آهن، اجزای پشتیبانی شبکه، یادگیری عمیق، محل هدف، تشخیص گسل
کلمات کلیدی انگلیسی railway, catenary support components, deep learning, target location, faults detection
شناسه دیجیتال – doi
https://doi.org/10.1109/I2MTC.2018.8409637
کد محصول E8669
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I. INTRODUCTION

ATENARY is an important part of the traction power supply system in high­speed railways, and it consists of some catenary support components (CSCs), such as Insulator, Rotary double ear, Brace sleeve. Fig. 1 shows the positions of all the crucial CSCs in the catenary system. The normal state of CSCs is the basis of the normal operation of high­speed trains. However, fault states of CSCs could occur due to the vibration caused by vehicles and the complex natural environment near the railway. Typical CSCs faults include breakage and fracture of the component, missing and looseness of the fasteners, and other fault states, as shown in Fig. 2. To ensure the power transmission from the catenary system to vehicles normally, it is essential to inspect the working states of CSCs. In this work, vision­based methods are generally used for CSCs inspection. The high­resolution cameras and LED lights are mounted on the top of a specific inspection vehicle, which moves along the railway and captures images of CSCs. Then intelligent algorithms are applied to process these images and detect the faults. Usually, the inspection of CSCs faults can be divided into two steps. First, CSCs are located in the raw image. Second, different fault detection methods are applied to different types of CSCs. At present, approaches are mainly based on the grayscale and edge information of located CSCs. If the location of CSCs is not accurate, the fault detection result may not be correct either. In the past decade, local feature descriptors such as SIFT (Scale­invariant feature transform) [1] descriptors, HOG (Histogram of Orientated Gradients) [2] features, LBP (Local Binary Pattern) [3] features and DPM (deformable part models) [4], have been popular and important methods in object location area. Although these methods can be used to locate CSCs, the accuracy needs to be improved when the image is complex. Besides, their processing speed is not fas enough for large amounts of captured images.