Bayesian Probabilistic Approach to Structural Health Monitoring

Bayesian Probabilistic Approach to Structural Health Monitoring

A Bayesian probabilistic methodology for structural health monitoring is presented. The method uses a sequence of identified modal parameter data sets to compute the probability that continually updated model stiffness parameters are less than a specified fraction of the corresponding initial model stiffness parameters. In this approach, a high likelihood of reduction in model stiffness at a lo...

A Bayesian Probabilistic Approach to Structural Health Monitoring

Some general issues associated with on-line structural health monitoring are discussed. In order to address the problem of determining the existence and location of damage in the presence of uncertainties, a global model-based structural health monitoring method which utilizes Bayesian probabilistic inference is developed. The results of tests using simulated data are described.

Bayesian Methods for Aircraft Structural Health Monitoring

Aircraft structures, whether metallic or composite, are subject to service damage which requires their periodic inspection and maintenance. While taking the aircraft out of service is quite costly, the assurance of structural integrity requires such inspection – and possible repair work based on the inspection results obtained. If damage is detected from an inspection, the decision whether to r...

Bayesian Computational Sensor Networks: Small-scale Structural Health Monitoring

The Bayesian Computational Sensor Network methodology is applied to small-scale structural health monitoring. A mobile robot, equipped with vision and ultrasound sensors, maps smallscale structures for damage (e.g., holes, cracks) by localizing itself and the damage in the map. The combination of vision and ultrasound reduces the uncertainty in damage localization. The data storage and analysis...

Bayesian Computational Sensor Networks for Aircraft Structural Health Monitoring