A computer-implemented method for determining an abnormal technical status of a technical system, comprising: receiving, from the technical system, a plurality of signals wherein each signal (S1 to Sn) is sampled over time and reflects the technical status of at least one system component retrieving from an alarm managing system associated with the technical system, high alarm thresholds (H1 to Hn) and low alarm thresholds (L1 to Ln), computing for each signal with associated high and low alarm thresholds obtained from the alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds (H1/L1) as the maximum of the distances between the value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the entire technical system; wherein the aggregate abnormality indicator at a particular sampling point is computed as: the Euclidian distance based on the univariate distances of the respective signals and the total number of signals, or a weighted Euclidian distance based on the univariate distances of the respective signals and the total number of signals wherein each univariate distance contribution is weighted with a weighting factor corresponding to the severity of an alarm associated with the respective signal as defined in the alarm management system; and wherein the AAI computation does not involve any pattern recognition; providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold, the abnormality threshold ensuring with a given probability that an aggregate abnormality indicator value, when being below the abnormality threshold, reflects normal operation of the technical system wherein the abnormal technical status is determined when the aggregate abnormality indicator exceeds the abnormality threshold. receiving, from the technical system, a plurality of signals wherein each signal (S1 to Sn) is sampled over time and reflects the technical status of at least one system component retrieving from an alarm managing system associated with the technical system, high alarm thresholds (H1 to Hn) and low alarm thresholds (L1 to Ln), computing for each signal with associated high and low alarm thresholds obtained from the alarm management system, at every sampling time point, a univariate distance to its associated alarm thresholds (H1/L1) as the maximum of the distances between the value of the respective signal and its associated alarm thresholds to quantify a degree of abnormality for the respective at least one system component; computing, at every sampling time point, based on the univariate distances at the respective sampling time points, an aggregate abnormality indicator reflecting the technical status of the entire technical system; wherein the aggregate abnormality indicator at a particular sampling point is computed as: the Euclidian distance based on the univariate distances of the respective signals and the total number of signals, or a weighted Euclidian distance based on the univariate distances of the respective signals and the total number of signals wherein each univariate distance contribution is weighted with a weighting factor corresponding to the severity of an alarm associated with the respective signal as defined in the alarm management system; and wherein the AAI computation does not involve any pattern recognition; providing, to an operator, a comparison of the aggregate abnormality indicator with a predetermined abnormality threshold, the abnormality threshold ensuring with a given probability that an aggregate abnormality indicator value, when being below the abnormality threshold, reflects normal operation of the technical system wherein the abnormal technical status is determined when the aggregate abnormality indicator exceeds the abnormality threshold.