Parallel data processing architectures for identification of structural modal properties using dense wireless sensor networks

摘要

With recent advances in wireless sensing and data acquisition technology,it has become feasible to instrument a large structure with a dense array of wireless sensors.Furthermore,the analog-to-digital conversion and data processing capabilities of current wireless sensing prototypes offer the ability to efficiently distribute data processing tasks across a large network of wireless sensing nodes.In this paper,three output-only system identification methods are modified for implementation in a distributed array of processors and embedded within the computational core of a network of wireless sensors.The embedded algorithms implemented include the peak peaking,random decrement and frequency domain decomposition methods for identification of structural modal parameters including modal frequencies,damping ratios and mode shapes.Emphasis is placed on parallel implementations of these typically centralized algorithms to ensure scalability of the approach to networks defined by high nodal densities.Using the balcony of a historic theatre as a testbed,a network of wireless sensors is installed and allowed to collect and process acceleration response data during a set of vibration tests so that modal parameters can be estimated by the network.