Online Tensor Decomposition with optimized Stochastic Gradient Descent: an Application in Structural Damage Identification

Structural Health Monitoring (SHM) provides an economic approach which aims to enhance understanding the behavior of structures by continuously collects data through multiple networked sensors attached to the structure. This data is then utilized to gain insight into the health of a structure and make timely and economic decisions about its maintenance. The generated SHM sensing data is non-stationary and exists in a correlated multi-way form which makes the batch/off-line learning and standard two-way matrix analysis unable to capture all of these correlations and relationships. In this sense, the online tensor data analysis has become an essential tool for capturing underlying structures in higher-order datasets. We propose a new optimized CP decomposition solver named OCPD for nonconvex problem based on stochastic gradient descent (SGD) algorithm. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets show that our method provides more accurate results compared with existing online tensor analysis methods.

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