Finite element and analytical computation of flash temperature

Sábitz, László; Kolonits, Ferenc

doi:10.3311/ppci.7531

Cited by 3 publications

(2 citation statements)

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“…Furthermore, the results were compared with many analytical solutions. Sábitz and Zobory [9] as well as Sábitz and Kolonits [10] also used FEM to investigate the temperature distribution on the surface of the wheel tread.…”

Section: Introductionmentioning

confidence: 99%

: FE modelling of a coupled thermal-elastic-plastic analysis in a rail-wheel contact

Zwierczyk¹,

Váradi²

2016

JCAM

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The aim of our analyses was to model the process of the thermal micro-crack development on and under a railway wheel tread, equipped only with a disk brake during an intensive braking procedure. Considering the fact that the thermal micro-crack development is a complex problem, a multistage finite element modelling technique was developed. Using the new modelling approach, the heat and the thermal stress development can be observed between the wheel and the rail using thermal and coupled thermal-elastic-plastic FE simulations.

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Section: Introductionmentioning

confidence: 99%

: FE modelling of a coupled thermal-elastic-plastic analysis in a rail-wheel contact

Zwierczyk¹,

Váradi²

2016

JCAM

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“…O estudo sobre o desgaste de rodas e os efeitos causados pelo contato com o trilho são comuns no domínio da engenharia ferroviária, baseando-se tradicionalmente tanto em modelos físicos-matemáticos, quanto em simulações computacionais, como análise de elementos finitos (SÁBITZ; KOLONITS, 2014).…”

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Interpretabilidade de modelos de aprendizado profundo aplicados ao diagnóstico e prognóstico não supervisionado de falhas.

Oliveira¹

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Failure diagnosis represents an important task for operational maintenance teams, focusing on the task of identifying the causes of equipment problems that can lead to deviations in expected behavior, as well as reducing expected efficiency. The application of detection and diagnostic techniques associated with predictive methods, commonly known as prognosis, enables a more accurate and adequate planning to deal with unexpected events that may put the system under study at risk. Through an early and detailed identification of possible causes and threats, maintenance teams can mobilize themselves in a more appropriate, planned and assertive manner, to deal with unwanted situations, before they actually occur, favoring greater system reliability and, consequently, avoiding unexpected service interruptions, reducing the possibility of material and human losses. Several techniques have been suggested in the literature to address questions about failure prognosis, with great emphasis on methods based on deep learning. Such methods are considered to have characteristics called "black box", as they do not provide the means to explain the results obtained, making it difficult to adopt informed and reliable decisions. Thus, this research proposes an unsupervised method for the diagnosis and prognosis of failures, based on deep learning techniques, which provide means to attest and contribute to the robustness of the results obtained, promoting greater confidence and assertiveness in the prediction and identification of possible problems. In the case study, real physical measurements of parameters of wheels and bearings of railroad cars carrying heavy loads are considered, captured from multiple track sensors coupled at specific points in a Brazilian railroad. Means are proposed that address restrictions in modeling with data and that attest to the reasonableness of the predictions made.

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Finite element method model to predict bulk and flash temperatures on polymer gears

Fernandes

Rocha

Martins

et al. 2018

Tribology International

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Finite element and analytical computation of flash temperature

Cited by 3 publications

References 0 publications

: FE modelling of a coupled thermal-elastic-plastic analysis in a rail-wheel contact

: FE modelling of a coupled thermal-elastic-plastic analysis in a rail-wheel contact

Interpretabilidade de modelos de aprendizado profundo aplicados ao diagnóstico e prognóstico não supervisionado de falhas.

Finite element method model to predict bulk and flash temperatures on polymer gears

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