Method for training machine learning models (51,57), comprising: - recording, by means of one or more computers, data obtained by means of one or more measuring devices, each in the form of a sensor for measuring a physical quantity, in the form of time series data, wherein the data comprise information on maintenance processes or conditions; - receiving, by means of the one or more computers, a plurality of classification data units relating to the data; characterized by - receiving, by the one or more computers and for each classification data unit, a selected part of the data; and - training, by means of the one or more computers, a plurality of machine learning models, each based on at least one of the classification data units and the corresponding at least one selected part of the data, wherein - the plurality of machine learning models represent a plurality of instances of the same machine learning model, wherein - the selected parts of the data are provided by a selection by a plurality of users via one or more interfaces, wherein one instance of the machine learning model is trained per user, wherein - a higher-level machine learning model is calculated from parameters of the plurality of machine learning models, and - the individual machine learning models are weighted with different weighting factors to calculate the higher-level machine learning model, wherein - the higher-level machine learning model is trained to classify, by means of the one or more computers, other data recorded by means of the one or more measuring devices and to generate a data set, by means of the one or more computers and based on the classification of the data, which indicates that maintenance work has been carried out. - recording, by means of one or more computers, data obtained by means of one or more measuring devices, each in the form of a sensor for measuring a physical quantity, in the form of time series data, wherein the data comprise information on maintenance processes or conditions; - receiving, by means of the one or more computers, a plurality of classification data units relating to the data; characterized by - receiving, by the one or more computers and for each classification data unit, a selected part of the data; and - training, by means of the one or more computers, a plurality of machine learning models, each based on at least one of the classification data units and the corresponding at least one selected part of the data, wherein - the plurality of machine learning models represent a plurality of instances of the same machine learning model, wherein - the selected parts of the data are provided by a selection by a plurality of users via one or more interfaces, wherein one instance of the machine learning model is trained per user, wherein - a higher-level machine learning model is calculated from parameters of the plurality of machine learning models, and - the individual machine learning models are weighted with different weighting factors to calculate the higher-level machine learning model, wherein - the higher-level machine learning model is trained to classify, by means of the one or more computers, other data recorded by means of the one or more measuring devices and to generate a data set, by means of the one or more computers and based on the classification of the data, which indicates that maintenance work has been carried out.