Smart Infrastructure Maintenance Using Incremental Tensor Analysis

Abstract

Civil infrastructures are key to the flow of people and goods in urban environments. Structural Health Monitoring (SHM) is a condition-based maintenance technology, which provides and predicts actionable information on the current and future states of infrastructures. SHM data are usually multi-way data which are produced by multiple highly correlated sensors. Tensor decomposition allows the learning from such data in temporal, spatial and feature modes at the same time. However, to facilitate a real time response for online learning, incremental tensor update need to be used when new data come in, rather than doing the decomposition in a batch manner. This work proposed a method called onlineCP-ALS to incrementally update tensor component matrices, followed by a self-tuning one-class support vector machine for online damage identification. Moreover, a robust clustering technique was applied on the tensor space for online substructure grouping and anomaly detection. These methods were applied to data from lab-based structures and also data collected from the Sydney Harbour Bridge in Australia. We obtained accurate damage detection accuracies for all these datasets. Damage locations were also captured correctly, and different levels of damage severity were well estimated. Furthermore, the clustering technique was able to detect spatial anomalies, which were associated with sensor and instrumentation issues. Our proposed method was efficient and much faster than the batch approach.