Siemens Mobility Gmbh

Maria Davidich

Udhayaraj Sivalingam

Dennis Klingebiel

Jennifer Ling

An accurate and reliable maintenance method for controlling a condition of a wear and tear element of a track-bound vehicle includes determining a second condition of the element, being a chronological subsequent condition to a first condition, by starting from the first condition of the element, by using a machine learning algorithm, representing a chronological behavior of the element. A first action performable on the element is determined by using the determined second condition of the element and at least one predefined conditional criterion for the element. A resulting third condition of the element is determined by using a conditional change of the element. The conditional change is a consequence of the first performable action. A second performable action performable on the element is determined one more time by using the resulting third condition of the element and at least one predefined conditional criterion for the element.

Condition controlling of a wear and tear element

An evaluation unit is fed with driving-related diagnostic data of a reference railway vehicle. The diagnostic data contain driving-related properties, on which a parameter of the wheel of the reference railway vehicle depends. The evaluation unit is also fed with wheel measurement data with measured values of the parameter of the wheel of the reference railway vehicle. The evaluation unit determines a function between the driving-related diagnostic data and the wheel measurement data via a supervised learning algorithm. The evaluation unit is fed with driving-related diagnostic data containing driving-related properties of an observed railway vehicle, on which a parameter of the wheel of the observed railway vehicle depends. The parameter of the wheel of the observed railway vehicle is determined by the evaluation unit using the determined function and the diagnostic data of the observed railway vehicle.

Method for determining a parameter of a wheel of an observed railway vehicle and evaluation unit

The life expectancy of a component of a vehicle that is being observed is predicted. A processing unit is fed with status data of components of selected vehicles. Moreover, the processing unit is fed with an operating parameter for each of the components of the selected vehicles. The operating parameter influences the status data of the respective component. The processing unit determines a function between the operating parameter and the status data for each of the selected components. One function, which fits best for the component of the observed vehicle, is selected by an algorithm. The processing unit is fed with an operating parameter of the component of the observed vehicle. The processing unit predicts the life expectancy of the component of the observed vehicle using the selected function and the operating parameter of the component of the observed vehicle.

Method for predicting the life expectancy of a component of an observed vehicle and processing unit

A method determines a duration of an embarking process and/or a disembarking process of at least one autonomously movable object conveyable by a movable unit. The method includes determining the duration of the embarking process and/or the disembarking process of at least one autonomously movable object embarking and/or disembarking the movable unit by using a model describing the embarking process and/or the disembarking process of at least one autonomously movable object embarking and/or disembarking the movable unit spatially discreetly dynamically. The determination method provides a flexible, reliable, time and cost saving operation of a travelling network.

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Munich, Germany

Maria Davidich