Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform

Rocha, Rafael L.; Silva, Cleison D.; Gomes, Ana; Ferreira, Bruno V.; Carvalho, Eduardo Atem de; Siravenha, Ana Carolina; Carvalho, Schubert R.

doi:10.5753/kdmile.2019.8786

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…The railway fleet consists of 300 locomotives and around 20,000 wagons, organized into compositions of 330 wagons each. Several studies have proposed the use of machine learning in the maintenance of these assets, as seen in works such as [Fernandes et al 2018] and [Rocha et al 2019].…”

Section: Introductionmentioning

confidence: 99%

A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting

Carvalho,

Oliveira,

Rocha

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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Analisar e prever a precipitação é crucial para a sociedade, principalmente quando ocorrem chuvas extremas e inundações. Tais eventos impactam as estruturas socioeconômicas e podem levar a fatalidades. O entendimento da formação das chuvas e das variáveis associadas auxilia no desenvolvimento de modelos preditivos dos níveis de precipitação, auxiliando na tomada de decisões nas cadeias produtivas e na mobilidade urbana. Avanços recentes neste campo resultam do aumento do poder de processamento do computador e da disponibilidade de dados meteorológicos em todo o mundo. Este estudo se concentra na avaliação dos modelos SARIMA, RNN e XGBoost para prever a precipitação mensal ao longo de uma ferrovia no norte do Brasil. Usando valores de precipitação defasados, SARIMA teve melhor desempenho em 11 dos 13 pontos (com R² variando de 0,704 a 0,817), enquanto RNN superou nos pontos restantes (15,39% dos pontos avaliados).

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Section: Introductionmentioning

confidence: 99%

A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting

Carvalho,

Oliveira,

Rocha

et al. 2023

Anais Do XX Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2023)

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A Deep Learning Approach Towards Railway Safety Risk Assessment

Alawad

Kaewunruen

2020

IEEE Access

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Railway stations are essential aspects of railway systems, and they play a vital role in public daily life. Various types of AI technology have been utilised in many fields to ensure the safety of people and their assets. In this paper, we propose a novel framework that uses computer vision and pattern recognition to perform risk management in railway systems in which a convolutional neural network (CNN) is applied as a supervised machine learning model to identify risks. However, risk management in railway stations is challenging because stations feature dynamic and complex conditions. Despite extensive efforts by industry associations and researchers to reduce the number of accidents and injuries in this field, such incidents still occur. The proposed model offers a beneficial method for obtaining more accurate motion data, and it detects adverse conditions as soon as possible by capturing fall, slip and trip (FST) events in the stations that represent high-risk outcomes. The framework of the presented method is generalisable to a wide range of locations and to additional types of risks.

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Towards a More Reliable Identification of Non-Conformities in Railway Cars: Experiments with Mask R-CNN, U-NET, and Ensembles on Unbalanced and Balanced Datasets

Carvalho,

Ferreira,

Gomes

et al. 2024

Sensors

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In every business, equipment requires repair services. Over time, equipment wears out; however, with well-conducted and guided maintenance, this degradation can be controlled, and failed equipment can be restored to operational status. Preventive maintenance allows this concept to be applied, given the great advantages for large companies in reusing equipment and machinery, always putting the worker’s health and safety first. Rail transport has several pieces of equipment that can be reused if they are in a regular and well-defined maintenance cycle. In this sense, this article sought to create a method using real data for identifying cracks in wagons. Through the use of computer vision algorithms to prepare the data, along with several machine learning classification algorithms to locate cracks in train cars, the classification used properly annotated images and obtained great results, with a best case 98.10% hit-rate when wagons had a crack problem.

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Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform

Cited by 3 publications

References 6 publications

A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting

A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting

A Deep Learning Approach Towards Railway Safety Risk Assessment

Towards a More Reliable Identification of Non-Conformities in Railway Cars: Experiments with Mask R-CNN, U-NET, and Ensembles on Unbalanced and Balanced Datasets

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