In this paper we summarize the hardware and software issues of impedance-based structural health monitoring based on piezoelectric materials. The basic concept of the method is to use high-frequency structural excitations to monitor the local area of a structure for changes in structural impedance that would indicate imminent damage. A brief overview of research work on experimental and theoretical studies on various structures is considered and several research papers on these topics are cited. This paper concludes with a discussion of future research areas and path forward.
Piezoelectric materials (PZT) can be used as mechanisms to transfer mechanical energy, usually ambient vibration, into electrical energy that can be stored and used to power other devices. With the recent advances in wireless and micro-electro-mechanical-systems (MEMS) technology, sensors can be placed in exotic and remote locations. As these devices are wireless it becomes necessary that they have their own power supply. The power supply in most cases is the conventional battery; however, problems can occur when using batteries because of their finite life span. Because most sensors are being developed so that they can be placed in remote locations such as structural sensors on a bridge or global positioning service (GPS) tracking devices on animals in the wild, obtaining the sensor simply to replace the battery can become a very expensive task. Furthermore, in the case of sensors located on civil structures, it is often advantageous to embed them, making access impossible. Therefore, if a method of obtaining the untapped energy surrounding these sensors was implemented, significant life could be added to the power supply. One method is to use PZT materials to obtain ambient energy surrounding the test specimen. This captured energy could then be used to prolong the power supply or in the ideal case provide endless energy for the sensors lifespan. The goal of this study is to develop a model of the PZT power harvesting device. This model would simplify the design procedure necessary for determining the appropriate size and vibration levels necessary for sufficient energy to be produced and supplied to the electronic devices. An experimental verification of the model is also performed to ensure its accuracy.
Based on the extensive literature that has developed on structural health monitoring over the last 20 years, it can be argued that this field has matured to the point where several fundamental axioms, or gen eral principles, have emerged. The intention of this paper is to explicitly state and justify these axioms. In so doing, it is hoped that two subsequent goals are facilitated. First, the statement of such axioms will give new researchers in the field a starting point that alleviates the need to review the vast amounts of literature in this field. Second, the authors hope to stimulate discussion and thought within the community regarding these axioms.
Piezoelectric materials can be used as a means of transforming ambient vibrations into electrical energy that can then be stored and used to power other devices. With the recent surge of microscale devices, piezoelectric power generation can provide a convenient alternative to traditional power sources used to operate certain types of sensors/actuators, telemetry, and MEMS devices. However, the energy produced by these materials is in many cases far too small to directly power an electrical device. Therefore, much of the research into power harvesting has focused on methods of accumulating the energy until a sufficient amount is present, allowing the intended electronics to be powered. In a recent study by Sodano et al. (2004a) the ability to take the energy generated through the vibration of a piezoelectric material was shown to be capable of recharging a discharged nickel metal hydride battery. In the present study, three types of piezoelectric devices are investigated and experimentally tested to determine each of their abilities to transform ambient vibration into electrical energy and their capability to recharge a discharged battery. The three types of piezoelectric devices tested are the commonly used monolithic piezoceramic material lead–zirconate–titanate (PZT), the bimorph Quick Pack (QP) actuator, and the macro-fiber composite (MFC). The experimental results estimate the efficiency of the three devices tested and identify the feasibility of their use in practical applications. Different capacity batteries are recharged using each device, to determine the charge time and maximum capacity battery that can be charged. The results presented in this article provide a means of choosing the piezoelectric device to be used and estimate the amount of time required to recharge a specific capacity battery.
The real-world structures are subjected to operational and environmental condition changes that impose difficulties in detecting and identifying structural damage. The aim of this report is to detect damage with the presence of such operational and environmental condition changes through the application of the Los Alamos National Laboratory's statistical pattern recognition paradigm for structural health monitoring (SHM). The test structure is a laboratory three-story building, and the damage is simulated through nonlinear effects introduced by a bumper mechanism that simulates a repetitive impact-type nonlinearity. The report reviews and illustrates various statistical principles that have had wide application in many engineering fields. The intent is to provide the reader with an introduction to feature extraction and statistical modelling for feature classification in the context of SHM. In this process, the strengths and limitations of some actual statistical techniques used to detect damage in the structures are discussed. In the hierarchical structure of damage detection, this report is only concerned with the first step of the damage detection strategy, which is the evaluation of the existence of damage in the structure. The data from this study and a detailed description of the test structure are available for download at: http://institute.lanl.gov/ei/software-and-data/.
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