Method of monitoring the condition of a fleet of plants, machines, or processes comprising the steps of - generating and storing a single generic model for all members of the fleet using selected historic data from the fleet taken during normal operation and using novelty detection, - scaling the selected historic data from the individual fleet members by using scaling factors derived from the historic data prior to including them in the single generic model, wherein scaling the input historic data includes subtracting from each data point a mean value and dividing by the standard deviation, forming clusters of the scaled data, determining a cluster center for each cluster, and calculating a cluster threshold for each cluster, wherein the cluster centers along with the calculated thresholds make up the model, - monitoring the fleet members during operation by taking test data, scaling the test data using said scaling factors, and comparing the scaled data to the generic model, - adapting the generic model during operation and monitoring by taking new data, standardizing the new data by subtracting from each data point a mean value and dividing by a standard deviation, and adding the new standardized data to the generic model while maintaining all previous data, and further adapting the scaling factor in the scaling file according to the new mean values and standard deviations of the new data, - giving a signal on the condition of the fleet members based on the comparison of the test data to the generic model. - generating and storing a single generic model for all members of the fleet using selected historic data from the fleet taken during normal operation and using novelty detection, - scaling the selected historic data from the individual fleet members by using scaling factors derived from the historic data prior to including them in the single generic model, wherein scaling the input historic data includes subtracting from each data point a mean value and dividing by the standard deviation, forming clusters of the scaled data, determining a cluster center for each cluster, and calculating a cluster threshold for each cluster, wherein the cluster centers along with the calculated thresholds make up the model, subtracting from each data point a mean value and dividing by the standard deviation, forming clusters of the scaled data, determining a cluster center for each cluster, and calculating a cluster threshold for each cluster, wherein the cluster centers along with the calculated thresholds make up the model, - monitoring the fleet members during operation by taking test data, scaling the test data using said scaling factors, and comparing the scaled data to the generic model, - adapting the generic model during operation and monitoring by taking new data, standardizing the new data by subtracting from each data point a mean value and dividing by a standard deviation, and adding the new standardized data to the generic model while maintaining all previous data, and further adapting the scaling factor in the scaling file according to the new mean values and standard deviations of the new data, - giving a signal on the condition of the fleet members based on the comparison of the test data to the generic model.