Bayesian probabilistic structural modeling for optimal sensor placement in ultrasonic guided wave-based structural health monitoring

Eric B. Flynn; Michael D. Todd

doi:10.1117/12.847744

8 April 2010 Bayesian probabilistic structural modeling for optimal sensor placement in ultrasonic guided wave-based structural health monitoring

Eric B. Flynn, Michael D. Todd

Author Affiliations +

Proceedings Volume 7648, Smart Sensor Phenomena, Technology, Networks, and Systems 2010; 76480Z (2010) https://doi.org/10.1117/12.847744
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2010, San Diego, California, United States

Abstract

Many optimal sensor placement methods for structural health monitoring establish performance metrics based on the detection of a limited set of damage states and locations. In guided wave-based inspection, however, monitoring is carried out over a continuous region with a continuous distribution of possible damage locations, types, sizes, and orientations. Here, traveling waves are excited and then received by a set of transducers with the intent of detecting and localizing previously unobserved scattering sources that are associated with damage. To measure sensor network performance in this application, we implement a Bayesian experimental design approach by computing the total posterior expected cost of detection over the entire monitoring region. Since the optimization usually must be carried out using a computationally expensive meta-heuristic such as a genetic algorithm, efficient modeling of the interrogation process is key to solving this distributed sensor placement problem. In this work, we implement a previously developed semi-analytical modeling approach for wave scattering within our Bayesian probabilistic framework in order to optimally place active sensors for detecting cracks of unknown location, size, and orientation. This involves assuming a set of a priori probability distributions on the three unknowns and defining spatial distributions of cost associated with type I and type II detection error. These parameters are driven by the geometry, material, in-service structural loading, and performance requirements of the structure. Through a set of sensor placement examples, we demonstrate how changes in the probability and cost distributions will dramatically alter the optimal layout of the transducer network.

Citation Download Citation

Eric B. Flynn and Michael D. Todd "Bayesian probabilistic structural modeling for optimal sensor placement in ultrasonic guided wave-based structural health monitoring", Proc. SPIE 7648, Smart Sensor Phenomena, Technology, Networks, and Systems 2010, 76480Z (8 April 2010); https://doi.org/10.1117/12.847744

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