Measurements of friction coefficients between rails lubricated with a friction modifier and the wheels of an IORE locomotive during real working conditions

Lundberg, Jan; Rantatalo, Matti; Wanhainen, Christina; Casselgren, Johan

doi:10.1016/j.wear.2014.12.002

Cited by 43 publications

(32 citation statements)

References 8 publications

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“…It is assumed that under these conditions, an over-application of a non-drying material could have significant impacts on the braking capabilities of a train. For example, it was reported that German railways experienced braking problems at some application sites where an unspecified TOR material was applied [20].…”

Section: Adhesion Conditionsmentioning

confidence: 99%

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Material concepts for top of rail friction management – Classification, characterisation and application

Stock

Stanlake

Hardwick

et al. 2016

Wear

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The concept of managing and adjustment of friction between the wheel and rail has a long history within the operation of railways systems. In the past, adjustment/management has been limited to gauge face lubrication and the use of sanding equipment. The introduction of the top of rail (TOR) friction modifier (FM) over the last 20 years now allows for the modification of the friction at the top of rail -wheel tread interface. This paper focusses on the concept of TOR friction adjustment. Recent developments have led to a new generation of products, defined here as, TOR lubricants (oil and/or grease-based) and hybrid materials (oil/water mixtures), which are non-drying or slow drying. Definitions and functional difference are detailed and contrasted with that of the water-based drying FM. The water-based TOR FM once applied rapidly dries, mixes with the existing third-body layer, and allows for the accommodation of shear displacement. TOR lubricants and hybrid materials rely on mixed boundary layer lubrication, contrary to application of the water-based TOR FM. It has been shown that the adhesion level is highly influenced by the lubricant application rates. The risks and benefits (lateral force reduction, corrugation mitigation, and impact on energy consumption and influence on rolling contact fatigue) are discussed for all product classifications. However, a lack of data exists for the TOR lubricants especially in the area of rolling contact fatigue where laboratory studies have identified the possibility of crack interaction. Whilst it can be seen that TOR lubricants have the ability to provide similar benefits to that of a water-based FM, they exhibit a strong dependency on the application rate which may lend itself to adhesion and RCF issues. Further work is recommended in this area.

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Section: Adhesion Conditionsmentioning

confidence: 99%

“…The same paper [20] also analysed the interaction of locomotive traction with a TOR Hybrid material. It was shown that even a slight over-application will cause extensive slippage problems for all the (driven) axles of the locomotive.…”

Section: Adhesion Conditionsmentioning

confidence: 99%

Material concepts for top of rail friction management – Classification, characterisation and application

Stock

Stanlake

Hardwick

et al. 2016

Wear

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“…Furthermore, wheel/rail friction management has a strong influence on the power consumption in the wheel/rail contact [14]. Friction modifiers can control the wheel/rail friction coefficient and the level of friction is significantly dependent on the amount applied top-of-rail (ToR) friction modifier [16].…”

Section: Introductionmentioning

confidence: 99%

Wear and damage transitions of wheel and rail materials under various contact conditions

Wang

Lewis

Yang

et al. 2016

Wear

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“…In rail traffic, the most accurate measurement of adhesion is obtained by using instrumented train that measures adhesion between actual wheels and rail [6].…”

Section: The Process Of Finding Factors Which Influence Adhesions Andmentioning

confidence: 99%

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

Juric

Danilchanka²,

Mousavi³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The popularity of learning and predictive technologies, across many problem domains, is unprecedented and it is often underpinned with the fact that we efficiently compute with vast amounts of data and data types, and thus should be able to resolve problems, which we could not in the past. This view is particularly common among scientists who believe that the excessive amount of data, we generate in real life, is ideal for performing predictions and training algorithms.However, the truth might be quite different.The paper illustrates the process of preparing a training data set for an ML classifier, which should predict certain conditions in mechanical engineering. It was not the case that it was difficult to define and choose classifiers, in order to secure safe predictions. It was our inability to create a safe, reliable and trustworthy training data set, from scientifically proven experiments, which created the problem. This places serious doubts on the way we use learning and predictive technologies today. It remains debatable what the next step should be. However, if in ML algorithms, and classifiers in particular, the semantic which is built-in data sets, influences classifier's definition, it would be very difficult to evaluate and rely on them, before we understand data semantics fully. In other words, we still do not know how the semantic, sometimes hidden in a data set, can adversely affect algorithms trained by them.

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Measurements of friction coefficients between rails lubricated with a friction modifier and the wheels of an IORE locomotive during real working conditions

Cited by 43 publications

References 8 publications

Material concepts for top of rail friction management – Classification, characterisation and application

Material concepts for top of rail friction management – Classification, characterisation and application

Wear and damage transitions of wheel and rail materials under various contact conditions

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

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