A computer-implemented method for detecting an anomalous operating status of a technical system, the method comprising steps of: in a training phase, obtaining a first set of time-series values generated by a digital twin simulation of the technical system for a regular operating status of the technical system, obtaining a second set of time-series values measured by a plurality of sensors, the plurality of sensors configured to monitor a set of operational parameters of the technical system, wherein the plurality of sensors collects the second set of time-series values in an anomalous operating status of the technical system, executing a training step by adjusting parameters of a machine learning model for detecting the regular operating status of the technical system and for discriminating data samples of the regular operating status from data samples of the anomalous operating status by processing triples of data samples, each of the triples of data samples comprising a first data sample and a second data sample both taken from the first set of time-series values, and a third data sample taken from the second set of time-series values, to generate a trained machine learning model, and in a monitoring phase, obtaining a set of multivariate time-series values measured by the plurality of sensors, calculating an anomaly score value for determining whether the technical system is in an anomalous operating status based on the obtained set of multi-variate time-series values and the trained machine learning model, generating and outputting a signal including information on the determined anomalous operating status of the technical system. in a training phase, obtaining a first set of time-series values generated by a digital twin simulation of the technical system for a regular operating status of the technical system, obtaining a second set of time-series values measured by a plurality of sensors, the plurality of sensors configured to monitor a set of operational parameters of the technical system, wherein the plurality of sensors collects the second set of time-series values in an anomalous operating status of the technical system, executing a training step by adjusting parameters of a machine learning model for detecting the regular operating status of the technical system and for discriminating data samples of the regular operating status from data samples of the anomalous operating status by processing triples of data samples, each of the triples of data samples comprising a first data sample and a second data sample both taken from the first set of time-series values, and a third data sample taken from the second set of time-series values, to generate a trained machine learning model, and in a monitoring phase, executing a training step by adjusting parameters of a machine learning model for detecting the regular operating status of the technical system and for discriminating data samples of the regular operating status from data samples of the anomalous operating status by processing triples of data samples, each of the triples of data samples comprising a first data sample and a second data sample both taken from the first set of time-series values, and a third data sample taken from the second set of time-series values, to generate a trained machine learning model, and in a monitoring phase, obtaining a set of multivariate time-series values measured by the plurality of sensors, calculating an anomaly score value for determining whether the technical system is in an anomalous operating status based on the obtained set of multi-variate time-series values and the trained machine learning model, generating and outputting a signal including information on the determined anomalous operating status of the technical system.