Design of structural monitoring sensor network using surrogate modeling of stochastic sensor signal

Abstract Efficient Structural Health Monitoring (SHM) could reduce operation and maintenance costs, improve longevity, and enhance safety of the performance in complex mechanical systems. Traditional sensor network for SHM relied on simple sensor behavior without considering uncertain factors from system and environment. A probabilistic sensing model is required to simulate the realistic and stochastic sensor performance in the sensor network design process. In this paper, we introduce reliability-based design optimization for piezoelectric sensor network considering the detectability of different failure modes. The proposed method diagnoses failure based on the Mahalanobis Distance (MD) suitable for many SHM processes, while considering the uncertainties from structure properties and operation condition. Kriging is applied for surrogate modeling of the stochastic sensor signal and reduce computational cost. The optimal piezoelectric sensor network design is prototyped and its failure detection capability is experimentally verified.