Evaluation of the Influence of Sensor Geometry and Physical Parameters on Impedance-Based Structural Health Monitoring

Abstract

Structural Health Monitoring (SHM) is the process of damage identification in mechanical structures that encompasses four main phases: damage detection, damage localization, damage extent evaluation and prognosis of residual life. Among various existing SHM techniques, the one based on electromechanical impedance measurements has been considered as one of the most effective, especially in the identification of incipient damage. This method measures the variation of the electromechanical impedance of the structure as caused by the presence of damage by using piezoelectric transducers bonded on the surface of the structure (or embedded into it). The most commonly used smart material in the context of the present contribution is the lead zirconate titanate (PZT). Through these piezoceramic sensor-actuators, the electromechanical impedance, which is directly related to the mechanical impedance of the structure, is obtained as a frequency domain dynamic response. Based on the variation of the impedance signals, the presence of damage can be detected. A particular damage metric can be used to quantify the damage. For the success of the monitoring procedure, the measurement system should be robust enough with respect to environmental influences from different sources, in such a way that correct and reliable decisions can be made based on the measurements. The environmental influences become more critical under certain circumstances, especially in aerospace applications, in which extreme conditions are frequently encountered. In this paper, the influence of electromagnetic radiation, temperature and pressure variations, and ionic environment have been examined in laboratory. In this context, the major concern is to determine if the impedance responses are affected by these influences. In addition, the sensitivity of the method with respect to the shape of the PZT patches is evaluated. Conclusions are drawn regarding the monitoring efficiency, stability and precision.

Impedance-based structural health monitoring and statistical method for threshold-level determination applied to 2024-T3 aluminum panels under varying temperature

“…Although equations ( 1) and (2) show several coupling terms, for standard piezoelectric materials, it is acceptable to assume that the magnetic effects are negligible. 12 Temperature effects, however, are not particularly negligible since the piezoelectric properties are significantly pyroelectric, 22 that is, a thermal change establishes an electric field and vice versa. The mechatronic model that describes the measurement process is shown in Figure 1 for a single-degree-of-freedom (DOF) system.…”

“…Temperature variation effects and compensation procedures for these effects have been widely studied. [5][6][7][8][9][10][11] Besides temperature variation effects, other environmental effects have been investigated for the ISHM technique, 12 which shows through analysis of variance that the temperature is the most significant effect that reflects on the impedance signatures.…”

Damage Localization and Quantification of Truss Structure Based on Electromechanical Impedance Technique and Neural Network

“…EMI technique has unique advantages, especially in the field of real-time health monitoring of some complex and irregular structures, such as truss structures [4] and connection bolts [5]. The influence of the added mass and shape of PZT patches was also reported [6].…”

Identificação de dano em estruturas utilizando uma metodologia que integra a técnica da impedância eletromecânica e ondas de Lamb

“…Uma grande desvantagem dos sistemas SHM ativos que usam transdutores piezelétricos é a influência da temperatura nos sensores. Alguma pequena variação de temperatura pode levar o sistema de SHM a fornecer diagnósticos falsos-positivos, ou seja, indicações positivas em casos onde nenhum dano mecânico existe, de fato, na estrutura monitorada (PALOMINO et al, 2012).…”

“…Já no processo de tomada de decisão, a estatística é uma aliada fundamental para o sucesso do esforço de monitoramento, sendo uma das principais ferramentas de SHM. Enquanto as técnicas de SHM YE, 2006, 2009, RAMADAS et al, 2011, MONNIER, 2006, LU et al, 2009INMAN, 2007, SOROHAN et al, 2011 Moura (2004Moura ( , 2008 e seguida dos trabalhos feitos por Palomino (2008Palomino ( , 2012, Tsuruta (2008), Leucas (2009) Foi mencionado que em um processo de SHM é necessário a tirada de atributos das medições obtidas pelos sensores responsáveis pelo monitoramento do sistema. A extração dos atributos remete aos modelos de reconhecimento de padrões, porém, em geral, o uso de modelos estatísticos é recorrente para os métodos que utilizam a análise de impedância de sensores.…”