Sparse and low-rank methods in structural system identification and monitoring
This paper presents sparse and low-rank methods for explicit modeling and harnessing the data structure to address the inverse problems in structural dynamics, identification, and data-driven health monitoring. In particular, it is shown that the structural dynamic features and damage information, intrinsic within the structural vibration response measurement data, possesses sparse and low-rank structure, which can be effectively modeled and processed by emerging mathematical tools such as sparse representation (SR), and low-rank matrix decomposition. It is also discussed that explicitly modeling and harnessing the sparse and low-rank data structure could benefit future work in developing data-driven approaches towards rapid, unsupervised, and effective system identification, damage detection, as well as massive SHM data sensing and management.
Nagarajaiah, Satish. "Sparse and low-rank methods in structural system identification and monitoring." Procedia Engineering, 199, (2017) Elsevier: 62-69. https://doi.org/10.1016/j.proeng.2017.09.153.