Abstract: A method, and a corresponding device and computer, determine a neural network configured to process temporal dependencies. The method includes providing input training data that comprises identification information of messages of a vehicle bus of a vehicle and information with respect to time intervals between the messages. The messages were transmitted in a first predetermined time period via the vehicle bus. The method also includes providing output training data that is representative of a bus load of the vehicle bus in a second predetermined time period, wherein the second predetermined time period is arranged temporally after the first predetermined time period. The method further includes determining the neural network as a function of the input training data and the output training data, and storing the determined neural network as a pre-trained neural network.

Abstract: A method for processing vehicle data includes receiving, by a computer system, via a first data transmission channel, a first subset of vehicle data recorded in a vehicle. The method also includes receiving, by the computer system, via a second data transmission channel, a second subset of the vehicle data. The method further includes selecting, by way of the computer system, data that are to be processed from the first and the second subset of the vehicle data on the basis of at least one predetermined selection criterion.

Abstract: A method for detecting abnormal operating states of a device includes obtaining model data to the device that is representative of operating states to be expected for at least one component of the device. The device collects measurement data that is representative of an actual operating state of the component of the device. The device ascertains comparison data on the basis of the model data and the measurement data, where the comparison data is representative of an expected operating state. The method includes using the comparison data and the measurement data as a basis for determining whether there is a discrepancy between the actual operating state and the expected operating state. The method further includes attributing an abnormal operating state to the at least one component in a manner corresponding to a time of collection of the measurement data on the basis of the discrepancy.