A Framework for the Use of Mobile Sensor Networks in System Identification

System identification (SID, also known as structural identification in this context) is the process of extracting a system’s modal properties from sensor measurements. Typically, a mathematical model is chosen for data fitting and the identification of model parameters yields modal property estimates. Historically, SID has relied on measurements from fixed sensors, which remain at specific locations throughout data collection. The ultimate flaw in fixed sensors is they provide restricted spatial information, which can be addressed by mobile sensors. In this dissertation, a framework is developed for extracting structural modal estimates from data collected by mobile sensors. The current state of mobile sensor networks applications in SHM is developing; research has been diverse, however limited. Reduced setup requirements for mobile sensor networks facilitate data collection, thus enable expedited information updates on a structure’s health and improved emergency response times to natural disasters. This research focuses on using mobile sensor data, i.e., data from sensors simultaneously recording in time, while moving in space, for comprehensive system identification of real structural systems. Mobile sensing data is analyzed from two perspectives, each requires different modeling techniques: an incomplete data perspective and a complete data perspective. In Chapter 2, Structural Identification using Expectation Maximization (STRIDE) is introduced, a novel application of the Expectation Maximization (EM) algorithm and approach for output-only modal identification. Chapter 3 revisits STRIDE for consideration of incomplete datasets, i.e., data matrices containing missing entries. Such instances may occur as a result of failed communications or packet losses in a wireless sensor network or as a result of sensing and sampling methods, e.g., mobile sensing. It is demonstrated that sensor network data containing a significant amount of missing observations can be used to achieve a comprehensive modal identification. Moreover, a successful real-world identification with simulated mobile sensors quantifies the preservation of spatial information, establishing benefits of this type of network, and emphasizing an inquiry for future SHM implementations. In Maximum Likelihood (ML) estimation theory, on which STRIDE is based, the precision of ML point estimates can be measured by the curvature of the likelihood function. Chapter 4 presents closed-form partial derivatives, observed information, and variance expressions for discrete-time stochastic state-space model entities. Confidence intervals are constructed for natural frequencies, damping ratios, and mode shapes using the asymptotic normality property of ML estimators. In anticipation of high-resolution scanning, mobile sensor data is also perceived to belong to a general class of data called dynamic sensor networks (DSNs), which inherently contain spatial discontinuities. Chapter 5 introduces state-space approaches for processing data from sensor networks with time-variant configurations for which a novel truncated physical model (TPM) is proposed. In typical state-space frameworks, a spatially dense observation space on the physical structure dictates a large state variable space, i.e., more total sensing nodes require a more complex dynamic model. The result is an overly complex dynamic model for the structural system. As sensor networks evolve and with increased use of novel sensing techniques in practice, it is desirable to decouple the size of the structural dynamic system from spatial sampling resolution during instrumentation. The TPM is presented as a novel technique to reduce physical state sizes and permit a general class of DSN data, with an emphasis on mobile sensing. Also, the role of basis functions in the approximation of mode shape regression is established. Chapter 6 discusses the identification of the TPM using an adjusted STRIDE methodology

Structural Health Monitoring and Application of Wireless Sensor Networks

Different elements of structural health monitoring (SHM) can benefit from the application of wireless sensor Networks (WSNs), as advanced sensing systems. While WSNs can significantly enhance the SHM by facilitating deployment of scalable and dense monitoring systems, challenges in the power consumption and data communication, and concerns regarding the possible impacts of their associated quality on the results have restricted their broad application. This research contributes in addressing the limitation associated with the prohibitive data communication delay and power consumption by introducing a novel time- and energy-efficient distributed algorithm for modal identification, and also addressing the concerns regarding the possible effects of their sensing quality by development of quality assessment approaches for modal identification and damage detection practices. The onboard processing techniques attempt to reduce the communication and power consumption by pushing the computation into the network. Efforts in developing onboard processing algorithms are restricted by the topology and algorithms, and their efficiency is not high enough to alleviate the challenge. A novel approach for modal identification of structural systems in a distributed scheme is developed which assigns the entire computational task of modal identification to remote nodes and limits the communication to transmission of only system\u27s parameters. The algorithm is based on estimation-updating steps at remote nodes and iterations by passing the results through the network for convergence of estimation. The algorithm is first developed for input-output scenarios and then is further expanded to address output-only systems as well. Development of approaches such as Cumulative System Formation for providing initial estimates of the system (as starting point of iteration) and also a novel AR-ARX approach for expediting the convergence also further enhanced the developed algorithm. Experiments and implementations have proved the functionality and performance of the algorithm. While the focus of the research is on development of algorithms for enhancing the application of wireless sensors in modal identification, other aspects of data-driven SHM such as damage detection, and performance evaluation through field-testing of real-life structures are also studied. A framework for damage detection algorithm including accuracy indicators and statistical approaches for change point detection is developed and validated through implementation on different experimental models. Moreover, the state of the art in structural monitoring and vibration evaluation is presented in two field deployments