A novel data-driven sensor placement optimization method for unsupervised damage detection using noise-assisted neural networks with attention mechanism

Optimization of sensor placement (OSP) is one of the important steps in structural health monitoring to reduce the instrumentation cost, and improve damage detection. Although modal analysis is an optional intermediate process in terms of damage detection, conventional OSP methods for damage detection are mostly dependent on mode shapes. In this case, the sensor placement and damage detection are highly relying on the accuracy of modal analysis, and the results may not be adapted to different type of excitations. In this article, a novel noise-assisted neural network with attention mechanism is proposed based on the above challenges. This method which can be used to optimize sensor placement in an unsupervised and data-driven manner, is verified using a dataset simulated from the ASCE benchmark and an experimental dataset obtained from shake table tests. The results from the simulated dataset show that the percentage of the sensors that could be removed are higher than the conventional effective independence (EFI) method, with a highest of 62.5% for cases with low noise levels. In the meantime, the occurrence and the level of damage can still be well detected with a reduced number of sensors. Most importantly, in the proposed approach the optimal sensor placement configurations can be determined adaptively to account for different forms of excitations and noise levels, which is impossible for conventional model-driven methods. The results obtained from the experimental dataset show that the proposed method is also effective for real-world applications. As a result, the proposed data-driven OSP method skips the conventional model analysis process and directly focuses on the sensor arrangement that enables accurate detection of damage. It also has the potential for application in related fields, such as the monitoring of aerospace and mechanical infrastructures.