Integrated Structural Health Assessment Using Piezoelectric Active Sensors

This paper illustrates an integrated approach for identifying structural damage. The method presented utilizes piezoelectric (PZT) materials to actuate/sense the dynamic response of the structures. Two damage identification techniques are integrated in this study, including impedance methods and Lamb wave propagations. The impedance method monitors the variations in structural mechanical impedance, which is coupled with the electrical impedance of the PZT patch. In Lamb wave propagations, one PZT patch acting as an actuator launches an elastic wave through the structure, and responses are measured by an array of PZT sensors. The changes in both wave attenuation and reflection are used to detect and locate the damage. Both the Lamb wave and impedance methods operate in high frequency ranges at which there are measurable changes in structural responses even for incipient damage such as small cracks, debonding, or loose connections. The combination of the local impedance method with the wave propagation based approach allows a better characterization of the system’s structural integrity. The paper concludes with experimental results to demonstrate the feasibility of this integrated active sensing technology

Structural health monitoring algorithm comparisons using standard data sets

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/

Vibration suppression in cutting tools using collocated piezoelectric sensors/actuators with an adaptive control algorithm

The machining process is very important in many engineering applications. In high precision machining, surface finish is strongly correlated with vibrations and the dynamic interactions between the part and the cutting tool. Parameters affecting these vibrations and dynamic interactions, such as spindle speed, cut depth, feed rate, and the part's material properties can vary in real-time, resulting in unexpected or undesirable effects on the surface finish of the machining product. The focus of this research is the development of an improved machining process through the use of active vibration damping. The tool holder employs a high bandwidth piezoelectric actuator with an adaptive positive position feedback control algorithm for vibration and chatter suppression. In addition, instead of using external sensors, the proposed approach investigates the use of a collocated piezoelectric sensor for measuring the dynamic responses from machining processes. The performance of this method is evaluated by comparing the surface finishes obtained with active vibration control versus baseline uncontrolled cuts. Considerable improvement in surface finish (up to 50%) was observed for applications in modern day machining

Damage Identification of Wind Turbine Blades Using Piezoelectric Transducers

This paper presents the experimental results of active-sensing structural health monitoring (SHM) techniques, which utilize piezoelectric transducers as sensors and actuators, for determining the structural integrity of wind turbine blades. Specifically, Lamb wave propagations and frequency response functions at high frequency ranges are used to estimate the condition of wind turbine blades. For experiments, a 1 m section of a CX-100 blade is used. The goal of this study is to assess and compare the performance of each method in identifying incipient damage with a consideration given to field deployability. Overall, these methods yielded a sufficient damage detection capability to warrant further investigation. This paper also summarizes the SHM results of a full-scale fatigue test of a 9 m CX-100 blade using piezoelectric active sensors. This paper outlines considerations needed to design such SHM systems, experimental procedures and results, and additional issues that can be used as guidelines for future investigations

Testing and Finite Element Analysis of an Inflatable Structure

info:eu-repo/semantics/publishe