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相关论文: Non-Destructive Rail Monitoring for Defect Identif…

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Nondestructive evaluation (NDE) techniques are widely used to detect flaws in critical components of systems like aircraft engines, nuclear power plants and oil pipelines in order to prevent catastrophic events. Many modern NDE systems…

统计方法学 · 统计学 2017-02-02 Ye Tian , Ranjan Maitra , William Q. Meeker , Stephen D. Holland

We consider the problem of distributed estimation of an unknown deterministic scalar parameter (the target signal) in a wireless sensor network (WSN), where each sensor receives a single snapshot of the field. We assume that the observation…

信息论 · 计算机科学 2015-10-09 Qing Zhou , Di Li , Soummya Kar , Lauren Huie , H. Vincent Poor , Shuguang Cui

Computer vision based methods have been explored in the past for detection of railway track defects, but full automation has always been a challenge because both traditional image processing methods and deep learning classifiers trained…

计算机视觉与模式识别 · 计算机科学 2018-11-27 Shruti Mittal , Dattaraj Rao

Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can both detect objects in the scene and estimate their distance…

Rail detection, essential for railroad anomaly detection, aims to identify the railroad region in video frames. Although various studies on rail detection exist, neither an open benchmark nor a high-speed network is available in the…

计算机视觉与模式识别 · 计算机科学 2023-04-13 Xinpeng Li , Xiaojiang Peng

A non-parametric complementary ensemble empirical mode decomposition (NPCEEMD) is proposed for identifying bearing defects using weak features. NPCEEMD is non-parametric because, unlike existing decomposition methods such as ensemble…

信号处理 · 电气工程与系统科学 2023-10-03 Anil Kumar , Yaakoub Berrouche , Radosław Zimroz , Govind Vashishtha , Sumika Chauhan , C. P. Gandhi , Hesheng Tang , Jiawei Xiang

Contemporary industrial Non-Destructive Inspection (NDI) methods require sensing capabilities that operate in occluded, hazardous, or access restricted environments. Yet, the current visual inspection based on optical cameras offers limited…

Regular inspection of rail valves and engines is an important task to ensure the safety and efficiency of railway networks around the globe. Over the past decade, computer vision and pattern recognition based techniques have gained traction…

Fault detection and diagnosis of the interconnects are crucial for prognostics and health management (PHM) of electronics. Traditional methods, which rely on electronic signals as prognostic factors, often struggle to accurately identify…

机器学习 · 计算机科学 2024-10-08 Tae Yeob Kang , Haebom Lee , Sungho Suh

This investigation attempts to create an automated framework for fault detection and organization for usage in contemporary radiography, as per NDE 4.0. The review's goals are to address the lack of information that is sufficiently…

计算机视觉与模式识别 · 计算机科学 2025-07-01 Aditya Sharma

Application of electronic railway systems as well as the implication of Automatic Train Control (ATC) System has increased the safety of rail transportation. However, one of the most important causes of accidents on the railway is rail…

计算机视觉与模式识别 · 计算机科学 2023-04-25 Mohsen Ebrahimi

Directed Energy Deposition (DED) offers significant potential for manufacturing complex and multi-material parts. However, internal defects such as porosity and cracks can compromise mechanical properties and overall performance. This study…

计算机视觉与模式识别 · 计算机科学 2025-02-12 Israt Zarin Era , Fan Zhou , Ahmed Shoyeb Raihan , Imtiaz Ahmed , Alan Abul-Haj , James Craig , Srinjoy Das , Zhichao Liu

Railway systems require regular manual maintenance, a large part of which is dedicated to inspecting track deformation. Such deformation might severely impact trains' runtime security, whereas such inspections remain costly for both finance…

机器学习 · 计算机科学 2021-05-11 Yutao Chen , Yu Zhang , Fei Yang

Accurate Defect detection is crucial for ensuring the trustworthiness of intelligent railway systems. Current approaches rely on single deep-learning models, like CNNs, which employ a large amount of data to capture underlying patterns.…

计算机视觉与模式识别 · 计算机科学 2023-11-28 Rahatara Ferdousi , Fedwa Laamarti , Chunsheng Yang , Abdulmotaleb El Saddik

A computationally method on damage detection problems in structures was conducted using neural networks. The problem that is considered in this works consists of estimating the existence, location and extent of stiffness reduction in…

神经与进化计算 · 计算机科学 2008-07-01 Ismoyo Haryanto , Joga Dharma Setiawan , Agus Budiyono

Railway axle maintenance is critical to avoid catastrophic failures. Nowadays, condition monitoring techniques are becoming more prominent in the industry to prevent enormous costs and damage to human lives. This paper proposes the…

Subsurface evaluation of railway tracks is crucial for safe operation, as it allows for the early detection and remediation of potential structural weaknesses or defects that could lead to accidents or derailments. Ground Penetrating Radar…

机器学习 · 计算机科学 2025-01-22 Farhad Kooban , Aleksandra Radlińska , Reza Mousapour , Maryam Saraei

Transmission lines are essential components in various signal and power distribution systems. In addition to their main use as connecting elements, transmission lines can also be employed as continuous sensors for the measurement and…

应用物理 · 物理学 2020-01-01 Stefan H. Strub , Lucas Böttcher

We propose a composite diagnostics solution for railway infrastructure monitoring. In particular, we address the issue of soft-fault detection in underground railway cables. We first demonstrate the feasibility of an orthogonal multitone…

系统与控制 · 电气工程与系统科学 2021-09-07 Rathinamala Vijay , Gautham Prasad , Yinjia Huo , SM Sachin , TV Prabhakar

We introduce a novel method, called Dispersion Entropy for Graph Signals, $DE_G$, as a powerful tool for analysing the irregularity of signals defined on graphs. We demonstrate the effectiveness of $DE_G$ in detecting changes in the…

组合数学 · 数学 2023-04-03 John Stewart Fabila-Carrasco , Chao Tan , Javier Escudero
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