A New Variable Step Size Adaptive Blind Sources Separation for Online Structural Modal Identification

The Equivariant adaptive separation by independence (EASI) algorithm, as an online blind structural identification method, is very important not only to better understand the structural response but also to conduct an efficient maintenance and management strategy. However, the traditional EASI algorithm has some drawbacks. It uses a constant step-size parameter and requires establishing a trade-off between the misadjustment in the steady-state and the convergence rate. This paper proposes a new variable step-size equivariant adaptive source separation via independence (VS-EASI) algorithm for online blind modal identification of structures. Unlike the traditional EASI algorithm,the proposed algorithm adaptively updates its step-size based on the input signals and the unmixing matrix, through establishing a new function between the step-size and the separating indicator. This results in a better performance for the proposed method, and fast convergence speed is achieved while the steady-state error is low. Furthermore, this algorithm mitigates the irrelevant noise, making it more suitable than the EASI algorithm for practical applications. Simulation results of synthetic examples and a benchmark structure verify the superior convergence and better performance of the proposed algorithm in the steady-state over the conventional EASI with a fixedstep-size in stationary environments as well as non-stationary ones.

کلیدواژه ها:

Adaptive blind sources separation ، Equivariant adaptive source separation via independence ، Modal identification ، On-line structural identification ، Variable step size adaptive algorithm