Abstract: A device method and computer program product are disclosed for trend prediction of the course of a time-dependent series of data points of a component or system, particularly for an aircraft or spacecraft, including: providing an optimised decision tree, the input node of which is provided for inputting an input vector, the nodes of which contain the data points of a respective input vector and the leaves of which each contain an extrapolation function; iteratively calculating future data points by a respective time-dependent series of data points being inputted into the decision tree as an input vector and the decision tree calculating therefrom a data point subsequent to the last data point of the input vector in an automated manner, the calculated subsequent data point being added to the time-dependent series of data points in order to be used as a new input vector for the next iteration step.

Abstract: A computer-implemented method, a computer program product and a device for the condition monitoring of a component or system, such as for an aircraft or spacecraft. The method includes: providing an optimized decision tree, the nodes of which contain attributes of an input vector, the leaves of which each contain a classification function and the connections of which between the nodes are weighted; providing an input vector derived from a data signal; implementing a condition monitoring in which the condition of the component or system is determined using the weighted decision tree and on the basis of the input vector. In addition to an item of information concerning which of the classification functions of the decision tree best corresponds to the input vector, an additional item of information concerning the probability with which the other classes of the decision tree correspond to the examined input vector is calculated and output.

Abstract: A computer-implemented method, a computer program product and a device for the condition monitoring of a component or system, such as for an aircraft or spacecraft. The method includes: providing an optimised decision tree, the nodes of which contain attributes of an input vector, the leaves of which each contain a classification function and the connections of which between the nodes are weighted; providing an input vector derived from a data signal; implementing a condition monitoring in which the condition of the component or system is determined using the weighted decision tree and on the basis of the input vector. In addition to an item of information concerning which of the classification functions of the decision tree best corresponds to the input vector, an additional item of information concerning the probability with which the other classes of the decision tree correspond to the examined input vector is calculated and output.

Abstract: A device method and computer program product are disclosed for trend prediction of the course of a time-dependent series of data points of a component or system, particularly for an aircraft or spacecraft, including: providing an optimised decision tree, the input node of which is provided for inputting an input vector, the nodes of which contain the data points of a respective input vector and the leaves of which each contain an extrapolation function; iteratively calculating future data points by a respective time-dependent series of data points being inputted into the decision tree as an input vector and the decision tree calculating therefrom a data point subsequent to the last data point of the input vector in an automated manner, the calculated subsequent data point being added to the time-dependent series of data points in order to be used as a new input vector for the next iteration step.