Increasing the efficiency and efficacy of second-order blind identification (SOBI) methods

Abstract

SUMMARYThis paper proposes a new technique to increase the efficiency and effectiveness of second-order blind identification (SOBI) methods by reducing the number of time-lagged covariance matrices required to produce highly accurate mixing matrix estimates. The technique is based on randomly selecting the time-lagged covariance matrices as opposed to choosing them sequentially, which takes advantage of a property of independence with regard to the selection of time-lagged covariance matrices, while simultaneously reducing the correlation between selected covariance matrices. The proposed randomized approach is first applied to undamped and damped sinusoids to demonstrate its effectiveness. The randomized method is then applied to a three-degree-of-freedom linear structure subjected to random excitation. Finally, the proposed method is incorporated into a modified version of the SOBI method and applied to perform identification of the UCLA Factor building from recorded earthquake responses. In each case, the performance of the randomized approach is compared with the traditional sequential selection of time-lagged covariance matrices, and the randomized approach consistently demonstrates superior performance both in terms of accuracy and efficiency. Additionally, the proposed randomized SOBI approach was easily inserted into the modified SOBI method demonstrating its malleability for SOBI variants and other blind source separation methods.

Shaking table tests for the experimental verification of the effectiveness of an automated modal parameter monitoring system for existing bridges in seismic areas

“…The algorithm is based on the combination of different OMA techniques in order to make possible the automatic analysis and interpretation of the stabilization diagram. The modal parameter estimation is basically carried out according to the covariance‐driven Stochastic Subspace Identification (SSI) method, but the preliminary Blind Source Separation (BSS), operated by the Second‐Order Blind Identification (SOBI) procedure, simplifies the identification of the physical poles; the interested reader can refer to Ghahari et al, Yang and Nagarajaiah, and Abazarsa et al for more details about applications of BSS and SOBI in the context of dynamic parameter identification and SHM. In fact, as a result of BSS, the raw data associated to the measured structural response are transformed into sources, which can be well‐separated (they show the contribution of a single mode to the structural response), not well‐separated (noise or minor contributions from other modes could be superimposed to the contribution of the main mode), or noise sources .…”

Partial source separation from unknown correlation mixture for eliminating unknown periodic disturbances from random measured signals

“…In this technique, it is assumed that all sources are fully uncorrelated and the intensity of white noises is relatively small for removal. The blind source separation technique has been applied to structural mode identification and anomaly detection based on extracted mixture by using response second-order statistics [14][15][16][17][18][19][20]. In those researches, all sources were separated except for small noises removed and particularly the sources were assumed as fully uncorrelated.…”