A method for identifying specific frequency components in a signal, the method comprising: obtaining the signal in the time domain, performing modal analysis on the signal by inputting the signal to a modal estimator, wherein a frequency spectrum of the signal is treated as a frequency response function of a virtual system, thereby identifying a plurality of mode estimates of the virtual system, for a plurality of modal orders, applying a method for the automatic detection of physical modes from the plurality of mode estimates for the plurality of modal orders, each mode estimate being defined by an eigenvalue and a mode shape, comprising the steps of: a) defining a number N of centroid values of N clusters, on the basis of one or more features of the mode estimates, b) distributing the mode estimates among the N clusters according to a non-hierarchical clustering algorithm based on said one or more features, c) defining a distance function D that expresses a comparison between a mode estimate of a given modal order and the same mode estimate of a lower modal order, and defining a threshold value Dt for said distance function, d) calculating the distance between multiple cluster pairs, as the distance function between representative mode estimates of the N clusters, e) if the distances between the multiple cluster pairs are equal to or above the threshold Dt, concluding that every cluster is associated to one particular mode and going to step g) f) if the distance between one or more cluster pairs is below the threshold, defining a common centroid for said cluster pairs and repeating step b), d) and e) with an amount of clusters lower than N, g) calculating a metric for spuriousness for all the mode estimates in the clusters, and deciding at least on the basis of the metric whether a cluster is associated to a physical mode or to a non-physical mode, wherein a mode is selected as physical when the values of the metric of all the mode estimates in a cluster are above a threshold, and selecting one or more physical modes, corresponding to said frequency components. obtaining the signal in the time domain, performing modal analysis on the signal by inputting the signal to a modal estimator, wherein a frequency spectrum of the signal is treated as a frequency response function of a virtual system, thereby identifying a plurality of mode estimates of the virtual system, for a plurality of modal orders, applying a method for the automatic detection of physical modes from the plurality of mode estimates for the plurality of modal orders, each mode estimate being defined by an eigenvalue and a mode shape, comprising the steps of: a) defining a number N of centroid values of N clusters, on the basis of one or more features of the mode estimates, b) distributing the mode estimates among the N clusters according to a non-hierarchical clustering algorithm based on said one or more features, c) defining a distance function D that expresses a comparison between a mode estimate of a given modal order and the same mode estimate of a lower modal order, and defining a threshold value Dt for said distance function, d) calculating the distance between multiple cluster pairs, as the distance function between representative mode estimates of the N clusters, e) if the distances between the multiple cluster pairs are equal to or above the threshold Dt, concluding that every cluster is associated to one particular mode and going to step g) f) if the distance between one or more cluster pairs is below the threshold, defining a common centroid for said cluster pairs and repeating step b), d) and e) with an amount of clusters lower than N, g) calculating a metric for spuriousness for all the mode estimates in the clusters, and deciding at least on the basis of the metric whether a cluster is associated to a physical mode or to a non-physical mode, wherein a mode is selected as physical when the values of the metric of all the mode estimates in a cluster are above a threshold, and a) defining a number N of centroid values of N clusters, on the basis of one or more features of the mode estimates, b) distributing the mode estimates among the N clusters according to a non-hierarchical clustering algorithm based on said one or more features, c) defining a distance function D that expresses a comparison between a mode estimate of a given modal order and the same mode estimate of a lower modal order, and defining a threshold value Dt for said distance function, d) calculating the distance between multiple cluster pairs, as the distance function between representative mode estimates of the N clusters, e) if the distances between the multiple cluster pairs are equal to or above the threshold Dt, concluding that every cluster is associated to one particular mode and going to step g) f) if the distance between one or more cluster pairs is below the threshold, defining a common centroid for said cluster pairs and repeating step b), d) and e) with an amount of clusters lower than N, g) calculating a metric for spuriousness for all the mode estimates in the clusters, and deciding at least on the basis of the metric whether a cluster is associated to a physical mode or to a non-physical mode, wherein a mode is selected as physical when the values of the metric of all the mode estimates in a cluster are above a threshold, and selecting one or more physical modes, corresponding to said frequency components.