Method for training machine learning models , comprising: - recording (S10), by means of one or more computers , data obtained by means of one or more measuring devices (60-62), 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 (S12), by means of the one or more computers , a plurality of classification data units relating to the data ; characterized by - receiving (S13), by the one or more computers and for each classification data unit, a selected part of the data ; and - training (S14), 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 (60-62) 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 (S10), by means of one or more computers , data obtained by means of one or more measuring devices (60-62), 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 (S12), by means of the one or more computers , a plurality of classification data units relating to the data ; characterized by - receiving (S13), by the one or more computers and for each classification data unit, a selected part of the data ; and - training (S14), 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 (60-62) 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. <EOS>