Online Tensor-Based Learning Model for Structural Damage Detection

Abstract

The online analysis of multi-way data stored in a tensor has become an essential tool for capturing the underlying structures and extracting the sensitive features that can be used to learn a predictive model. However, data distributions often evolve with time and a current predictive model may not be sufficiently representative in the future. Therefore, incrementally updating the tensor-based features and model coefficients are required in such situations. A new efficient tensor-based feature extraction, named Nesterov Stochastic Gradient Descent (NeSGD), is proposed for online (CP) decomposition. According to the new features obtained from the resultant matrices of NeSGD, a new criterion is triggered for the updated process of the online predictive model. Experimental evaluation in the field of structural health monitoring using laboratory-based and real-life structural datasets shows that our methods provide more accurate results compared with existing online tensor analysis and model learning. The results showed that the proposed methods significantly improved the classification error rates, were able to assimilate the changes in the positive data distribution over time, and maintained a high predictive accuracy in all case studies.