A study and extension of second-order blind source separation to operational modal analysis

Second-order blind source separation (SOBSS) has gained recent interest in operational modal analysis (OMA), since it is able to separate a set of system responses into modal coordinates from which the system poles can be extracted by single-degree-of-freedom techniques. In addition, SOBSS returns a mixing matrix whose columns are the estimates of the system mode shapes. The objective of this paper is threefold. First, a theoretical analysis of current SOBSS methods is conducted within the OMA framework and its precise conditions of applicability are established. Second, a new separation method is proposed that fixes current limitations of SOBSS: It returns estimate of complex mode shapes, it can deal with more active modes than the number of available sensors, and it shows superior performance in the case of heavily damped and/or strongly coupled modes. Third, a theoretical connection is drawn between SOBSS and stochastic subspace identification (SSI), which stands as one of the points of reference in OMA. All approaches are finally compared by means of numerical simulations.