Research in damage detection and structural health monitoring in engineering systems during their service life has received increasing attention because of its importance and benefits in maintenance and rehabilitation of structure. Though the concept of vibration-based damage detection has been in existence for decades, and several procedures have been proposed to date, its practical applications remain limited, considering the increased utilization of sensors to measure structural response at multiple points. In this thesis, use of acceleration response of the structure as a method of global damage detection is explored using instantaneous frequency and stiffness degradation methods. Instantaneous frequency was estimated using continuous wavelet transform of measured acceleration response of the structure subjected to ground motion. Complex Morlet Wavelet was used in the time-frequency analysis due to its ability to provide sufficient resolution in both time and frequency domains. This ability is important in analyzing nonstationary signals like earthquake response of structure containing sharp changes in the signal. The second method, called the stiffness degradation analysis, is based on estimating the time-varying stiffness. This estimation is done by fitting a moving least-square line to the force-displacement loop for the duration of the ground motion.A four-story shear building is used as the model structure for numerical analysis. Two damage scenarios are considered: single damage instant and multiple damage instants. Both scenarios assume that the damage occurs at a single location. In the numerical simulations, damage was modeled as a reduction in the stiffness of the first floor, and accelerations were computed at floor levels using state-space model. The two methods were compared in terms of their damage detection ability and it was shown that both methods can be used in detecting damage and the time at which the damage occurs. These methods can later be extended by simultaneously considering the correlations of responses at all floor levels. This extension may enable locating the damage and quantifying the severity of the damage.

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This thesis explores the application of the Virtual Test Bed (VTB) and its real-time extension, VTB-RT, for protective relay modeling, simulation, and testing. An instantaneous overcurrent relay model was developed in VTB for a transmission line protection. The same relay model was built in Matlab/Simulink for validation purposes. Both models were tested for various fault conditions on a radial power system and results were compared. Moreover, a low cost real-time Hardware-In-the-Loop (HIL) simulation platform was implemented for relay model testing using VTB-RT and public domain software packages such as Real-Time Application Interface (RTAI), Comedi, and Comedilib, and notebook computer hardware. The applicability of VTB-RT was verified through an open-loop simulation and a HIL simulation of a simple dynamic system using dSPACE as the control hardware and NI DAQCard-6062E as the input/output interface. Simulation results are presented showing the effectiveness of the VTB-RT platform for model testing.

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The research presented in this thesis is based on improving current structural health monitoring (SHM) technology. Structural health monitoring is a damage detection technique that involves placing intelligent sensors on a structure, periodically recording data from the sensors, and using statistical methods to analyze the data in order to assess the condition of the structure. This work focuses on improving two areas of SHM; baseline management and energy supplies. Several successful SHM methods have been developed in which prerecorded baseline measurements are compared to current measurements in order to identify damage. The need to compare new data to a prerecorded baseline can present several complications including data management issues and difficulty in controlling the effects of varying environmental conditions on the data. Another potential area for improvement in SHM systems deals with their energy supplies. Many SHM systems currently require wired power supplies or batteries to operate. Practical SHM applications often require inexpensive, stand alone sensors, data acquisition, and processing hardware that does not require maintenance. To address the issue of baseline management, a novel SHM technique is developed. This new method accomplishes instantaneous baseline measurements by deploying an array of piezoelectric sensors/actuators used for Lamb wave propagation-based SHM such that data recorded from equidistant sensor-actuator paths can be used to instantaneously identify several common features of undamaged paths. Once identified, features from these undamaged paths can be used to form a baseline for real-time damage detection. This method utilizes the concept of sensor diagnostics, a recently developed technique that minimizes false damage identification and measurement distortion caused by faulty sensors. Several aspects of the instantaneous baseline damage detection method are explored in this work including the implementation of sensor diagnostics, determination of the features best used to identify damage, development of signal processing algorithms used to analyze data, and the comparison of two sensor/actuator deployment schemes. The ultimate goal in the development of practical SHM systems is to create autonomous damage detection systems. A limiting factor in current SHM technology is the energy supply required to operate the system. Many existing SHM systems utilize wired power supplies or batteries to power sensors, data transmission, data acquisition, and data processing hardware. Although batteries eliminate the need to run wires to SHM hardware, their periodic replacement requires components to be placed in easily accessible locations which is not always practical, especially in embedded applications. Additionally, there is a high cost associated with battery monitoring and replacement. In an effort to eliminate replaceable energy supplies in SHM systems, the concept of energy harvesting is investigated. Energy harvesting devices are designed to capture surrounding ambient energy and convert it into usable electrical energy. Several types of energy harvesting exist, including vibration, thermal, and solar harvesting. A solar energy harvesting system is developed for use in powering SHM hardware. Integrating energy harvesting technology into SHM systems can provide autonomous health monitoring of structures. / Master of Science

Instantaneous Frequency (IF) estimation based on time-varying autoregressive (TVAR) modeling has been shown to perform well in practical scenarios when the IF variation is rapid and/or non-linear and only short data records are available for modeling. A challenging aspect of implementing IF estimation based on TVAR modeling is the efficient computation of the time-varying coefficients by solving a set of linear equations referred to as the generalized covariance equations. Conventional approaches such as Gaussian elimination or direct matrix inversion are computationally inefficient for solving such a system of equations especially when the covariance matrix has a high order. We implement two recursive algorithms for efficiently inverting the covariance matrix. First, we implement the Akaike algorithm which exploits the block-Toeplitz structure of the covariance matrix for its recursive inversion. In the second approach, we implement the Wax-Kailath algorithm that achieves a factor of 2 reduction over the Akaike algorithm in the number of recursions involved and the computational effort required to form the inverse matrix. Although a TVAR model works well for IF estimation of frequency modulated (FM) components in white noise, when the model is applied to a signal containing a finitely correlated signal in addition to the white noise, estimation performance degrades; especially when the correlated signal is not weak relative to the FM components. We propose a decorrelating TVAR (DTVAR) model based IF estimation and a DTVAR model based linear prediction error filter for FM interference rejection in a finitely correlated environment. Simulations show notable performance gains for a DTVAR model over the TVAR model for moderate to high SIRs. / Master of Science