A quasi-recursive correlation dimension analysis for online structural health monitoring (SHM)
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abstract
The nonlinear and nonstationary nature of structural damage (bridges, buildings, etc.) brings a great challenge to structural health monitoring (SHM). The correlation dimension is an effective index to capture the process nonstationarity. But the traditional algorithms for computation of correlation dimension have fairly high complexity O(N2)and thus are not suitable for online monitoring. To tackle this challenge, this paper presents a novel quasi-recursive correlation dimension algorithm (QRCD) for online damage detection of structures. It can significantly alleviate the complexity of computation for correlation dimension to approximate O(N), and thus, make the online monitoring of nonlinear/nonstationary processes using correlation dimension much more applicable and efficient. The case studies show that for detection of process nonstationarity (occurrence of damages), the EWMA control charts using correlation dimension have shorter average run length (ARL) than the control charts using wavelet coefficients. And the proposed method significantly reduces the computational complexity in terms of computation time by approximately 90% (for different levels of damage) as opposed to the traditional methods. Moreover, the developed methodology is less influenced by process noise compared to the wavelet analysis based approaches. All these results demonstrate that our proposed method is both effective and efficient for online SHM.
