Online and real-time damage calculation in automotive transmissions Application to remaining service life estimation

Foulard, Stéphane; Rinderknecht, Stephan; Perret-Liaudet, Joël; Ichchou, Mohamed

doi:10.1533/9781782421955.933

Cited by 2 publications

(1 citation statement)

References 4 publications

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“…Publications by Foulard et al [3][4][5][6] presented a method using a signal of the input torque of a vehicle gearbox to constantly estimate its state of damage. The remaining service-life is also predicted with this approach.…”

Section: Condition Monitoring and Predictive Maintenancementioning

confidence: 99%

Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

Vietze

Hein

Stahl

2020

Forsch Ingenieurwes

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Most vehicle-gearboxes operating today are designed for a limited service-life. On the one hand, this creates significant potential for decreasing cost and mass as well as reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing operating time of the machine. Especially if a failure can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and, on the other hand, the probability of a failure increases with longer operating times. Therefore, a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible. Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, there is very little possibility to validate the technical design during operation, today. Hence, the goal of this paper is to present a method, enabling the prediction of the remaining-service-life and state-of-health of gears during operation. Within this method big-data and machine-learning approaches are used. The method is designed in a way, enabling an easy transfer to other machine elements and kinds of machinery.

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Section: Condition Monitoring and Predictive Maintenancementioning

confidence: 99%

Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

Vietze

Hein

Stahl

2020

Forsch Ingenieurwes

View full text Add to dashboard Cite

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Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

Vietze,

Pellkofer,

Stahl

2024

Machines

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Calculating the service life of gears under variable loads requires a description of the load-carrying capacity. The current standard for this is the use of the S/N curve. International standards such as ISO 6336 stipulate the use of this approach for the calculation of the service of gears under variable loads. In this paper, five new approaches are developed and evaluated to describe the load-carrying capacity of gears in the load range of finite life. Four methods are based on machine learning, and one uses mathematical regression. To validate the new approaches, the results of an experimental study investigating the service life of gears under variable loads are presented. These results form the basis for the conducted study, which compares the five new methods with the existing approach. The comparison focuses on the ability of the load-carrying capacity descriptions to provide an accurate calculation of the service life and to reduce scattering as much as possible. The results of the study show significant potential for the new methods, especially the one based on a neural network.

show abstract

Online and real-time damage calculation in automotive transmissions Application to remaining service life estimation

Cited by 2 publications

References 4 publications

Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

Method for a cloud based remaining-service-life-prediction for vehicle-gearboxes based on big-data-analysis and machine learning

Study on the Potential of New Load-Carrying Capacity Descriptions for the Service Life Calculations of Gears

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