Probabilistic fatigue assessment for railway axles and derivation of a simple format for damage calculations

Beretta, S.; Regazzi, D.

doi:10.1016/j.ijfatigue.2015.08.010

Cited by 41 publications

(16 citation statements)

References 18 publications

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“…For a further validation of the proposed probabilistic control volume method, it is used to predict the fatigue strength of the full scale EA4T axles by using the fatigue strength data tested for small specimens in literature. Figure 10 shows a comparison of the predicted P-S-N curves with the experimental data for the small specimen in literature [24], in which a = −0.0487, B = 2.572 and N 0 = 1.66 × 10 6 . It is seen that, the predicted 50% survival probability curve is generally in the middle of the scattered experimental data, and most of the experimental data are within the predicted 95% and 5% survival probability curves, indicating that the predicted P-S-N curves accord well with the experimental data.…”

Section: Comparison With Experimental Data Of Full Scale Axlesmentioning

confidence: 99%

“…It is seen that, the predicted 50% survival probability curve is generally in the middle of the scattered experimental data, and most of the experimental data are within the predicted 95% and 5% survival probability curves, indicating that the predicted P-S-N curves accord well with the experimental data. Figure 11 shows the comparison of the predicted P-S-N curves with the experimental data of the full scale EA4T axles [24] by using the fatigue strength data of the small specimen [24]. Here, the control surface is considered, which is 9656.84 mm 2 for the full scale axle and 227.85 mm 2 for the small specimen.…”

Section: Comparison With Experimental Data Of Full Scale Axlesmentioning

confidence: 99%

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A Method for Predicting the Effects of Specimen Geometry and Loading Condition on Fatigue Strength

Sun

Song

2018

Metals

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Specimen geometry and loading condition usually have a great influence on the fatigue strength of metallic materials, which is an important issue in evaluating the reliability of component parts. In this paper, a rotating bending fatigue test is performed at first on an hourglass specimen and a notch specimen of a high strength titanium alloy. Experimental results indicate that, in terms of local stress, the notch specimen endures higher fatigue strength in comparison with the hourglass specimen due to its relatively smaller control volume. Then, a probabilistic control volume method is proposed for correlating the effects of specimen geometry and loading condition on the fatigue strength based on Weibull distribution and the concept of control volume. A simple formula is obtained for the fatigue strength in relation to control volumes, in which the parameter is the shape parameter of Weibull distribution of fatigue strength. The predicted results are in good agreement with the present experimental data for high strength titanium alloy and the data for the high strength steel and the full scale EA4T axle in the literature.

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Section: Comparison With Experimental Data Of Full Scale Axlesmentioning

confidence: 99%

Section: Comparison With Experimental Data Of Full Scale Axlesmentioning

confidence: 99%

A Method for Predicting the Effects of Specimen Geometry and Loading Condition on Fatigue Strength

Sun

Song

2018

Metals

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“…From the tests performed in Euraxles on EA1N, a set of parameters is finally defined in the project and is recommended for such a steel grade. * The proposed method is finally quite similar to the one proposed by Beretta which is based on the Eurocode recommendations [21]. The main difference lays on the consideration of the load spectra variability: Beretta's proposal is based on a representative load spectrum, with a given and fixed shape, to which an assumed scatter (the same for all load classes) is associated, while in the approach presented in this paper, the load spectra can have different shapes, depending on the load historical signals.…”

Section: Discussionmentioning

confidence: 65%

“…Various New tests, performed in the research project Euraxles on small scale specimen, in constant and variable amplitudes configurations, led to a new set of parameters, agreed to be the reference final set in the project Euraxles [21,22].…”

Section: Fatigue Reliability Assessment Of a Railway Passenger Coach mentioning

confidence: 99%

EURAXLES - A global approach for design, production and maintenance of railway axles: WP1 - Advances in fatigue load analysis and reliability assessment of railway axles

Nguyen-Tajan

Lorang

2017

Mat.-wiss. u. Werkstofftech.

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The work presented in this paper was led within the collaborative project “Euraxles” of the FP7 program of the European Commission. It aimed at developing processes and methods that contribute to the minimization of the risk of fatigue failure of railway axles in service. This paper focuses on the development of a method to assess the reliability of axles according to fatigue damage. The proposed approach is mainly based on the stress strength interference analysis (SSIA) and the fatigue‐equivalent‐load (FEL) methods. It aims at calculating the axles’ probability of fatigue failure, by characterizing the variability of real in‐service loads and the scatter of the axles fatigue strength, and at evaluating more accurately the actual design margins. First of all, the main lines of the stress strength interference analysis method are recalled. This method aims at evaluating the in‐service reliability of components for their design or their homologation. It is used in many industries for various applications (mechanical components or systems, electronic elements, etc.). In the second part, the fatigue load analysis method that is proposed for railway axles is described. It starts with a post‐processing of an axle load measurement: from a time signal of forces applied to both wheels fitted on the axle, fatigue cycles of bending moment applied to the axle are identified and transformed into a cyclic equivalent load, the Meq, which is a measurement of the severity of the initial variable load. Then, virtual but realistic load spectra are generated, thanks to a classification operation followed by a random draw of elementary load data that considers the operation and maintenance conditions of the axle. All the spectra are then analysed thanks to the fatigue‐equivalent‐load method in order to build the distribution of in‐service load severities that gives a picture of the stress to which the axles are submitted. In the third and last part of the paper, the methods are applied to real data of “Société nationale des chemins de fer français” (SNCF), the French national railway operator. Sensitivity analyses are performed in order to quantify the effect on the Meq of variations of parameters and to verify the convergence and robustness of the process. Finally, results obtained for a passenger coach are given. The comparison between the distribution of load severities and the normative load, defined as according to european standards EN13103, shows that, for the studied axle, the normative load is very conservative. Using the axles fatigue limits identified on full‐scale tests, a stress strength interference analysis is performed to calculate the probability of failure of the axle.

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“…Beretta and Regazzi [2] developed a probabilistic extension to the standard railway axle calculation method which computes failure probability utilizing Monte Carlo simulations considering the position of the S-N curve and the load spectrum as random variables. Kim et al [3] analyzed multiple crack growth with the NASGRO model and XFEM simulations considering the variability of initial crack length, material parameters and crack growth rates.…”

Section: Introductionmentioning

confidence: 99%

Fatigue design of cast iron components with surface discontinuities

2018

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Abstract. Surface discontinuities are unavoidable properties of production castings, and have fundamental effect on in-service reliability. In the current study a multiaxial probabilistic fatigue analysis model has been developed for the description of local (e.g.: notches or surface defects) and structural gradient (e.g: bending) effects on crack initiation life. Identification of the model parameters and the validation has been done on the ferrite-pearlitic NCI grade EN GJS 500-7. The survival probability type simulation results are proven to be highly accurate with 5% average, and 11% maximum prediction error. In theory, the model might be able to assess the effect of arbitrarily complex defects on the fatigue strength, which is to be investigated in the future.

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Probabilistic fatigue assessment for railway axles and derivation of a simple format for damage calculations

Cited by 41 publications

References 18 publications

A Method for Predicting the Effects of Specimen Geometry and Loading Condition on Fatigue Strength

A Method for Predicting the Effects of Specimen Geometry and Loading Condition on Fatigue Strength

EURAXLES - A global approach for design, production and maintenance of railway axles: WP1 - Advances in fatigue load analysis and reliability assessment of railway axles

Fatigue design of cast iron components with surface discontinuities

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