Truncated Physical Model for Dynamic Sensor Networks with Applications in High-Resolution Mobile Sensing and BIGDATA

Historically, structural health monitoring (SHM) has relied on fixed sensors, which remain at specific locations in a structural system throughout data collection. This paper introduces state-space approaches for processing data from sensor networks with time-variant configurations, for which a novel truncated physical model (TPM) is proposed. The state-space model is a popular representation of the second-order equation of motion for a multidegree of freedom (MDOF) system in first-order matrix form based on field measurements and system states. In this mathematical model, a spatially dense observation space on the physical structure dictates an equivalently large modeling space, i.e.,more total sensing nodes require a more complex dynamic model. Furthermore, such sensing nodes are expected to coincide with state variable DOF. Thus, the model complexity of the underlying dynamic linear model depends on the spatial resolution of the sensors during data acquisition. As sensor network technologies evolve and with increased use of innovative sensing techniques in practice, it is desirable to decouple the size of the dynamic system model from the spatial grid applied through measurement. This paper defines a new data class called dynamic sensor network (DSN) data, for efficiently storing sensor measurements from a very dense spatial grid (very many sensing nodes). Three exact mathematical models are developed to relate observed DSN data to the underlying structural system. Candidate models are compared from a computational perspective and a truncated physical model (TPM) is presented as an efficient technique to process DSN data while reducing the size of the state variable. The role of basis functions in the approximation of mode shape regression is also established. Two examples are provided to demonstrate new applications of DSN that would otherwise be computationally prohibitive: high-resolution mobile sensing and BIGDATA processing.