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.