Samuel da Silva

Structural Health Monitoring in Smart Structures Through Time Series Analysis

This paper describes the application of a structural health monitoring technique based on electrical measurements obtained by piezoceramics (PZT) patches bonded in lightweight structures. The goal is to detect and locate imminent structural change occurrence with statistical confidence through a nondestructive evaluation test. Though the major focus in damage detection is given by monitoring electrical impedance in frequency-domain, the current research work applies a novel approach based on time-series. In such case, auto-regressive moving average with exogenous input (ARMAX) system identification models and statistical process control (SPC) charts are used for linear prediction to detect and locate damages. In order to compare the results, the classical damage metric chart obtained by frequency response from input–output data is described. The efficacy of the proposed approach is demonstrated through experimental tests.This paper describes the application of a structural health monitoring technique based on electrical measurements obtained by piezoceramics (PZT) patches bonded in lightweight structures. The goal is to detect and locate imminent structural change occurrence with statistical confidence through a nondestructive evaluation test. Though the major focus in damage detection is given by monitoring electrical impedance in frequency-domain, the current research work applies a novel approach based on time-series. In such case, auto-regressive moving average with exogenous input (ARMAX) system identification models and statistical process control (SPC) charts are used for linear prediction to detect and locate damages. In order to compare the results, the classical damage metric chart obtained by frequency response from input—output data is described. The efficacy of the proposed approach is demonstrated through experimental tests.

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure.

Robust control to parametric uncertainties in smart structures using linear matrix inequalities

The study of algorithms for active vibrations control in flexible structures became an area of enormous interest, mainly due to the countless demands of an optimal performance of mechanical systems as aircraft and aerospace structures. Smart structures, formed by a structure base, coupled with piezoelectric actuators and sensor are capable to guarantee the conditions demanded through the application of several types of controllers. This article shows some steps that should be followed in the design of a smart structure. It is discussed: the optimal placement of actuators, the model reduction and the controller design through techniques involving linear matrix inequalities (LMI). It is considered as constraints in LMI: the decay rate, voltage input limitation in the actuators and bounded output peak (output energy). Two controllers robust to parametric variation are designed: the first one considers the actuator in non-optimal location and the second one the actuator is put in an optimal placement. The performance are compared and discussed. The simulations to illustrate the methodology are made with a cantilever beam with bonded piezoelectric actuators.

Design of a Control System using Linear Matrix Inequalities for the Active Vibration Control of a Plate

The study of algorithms for active vibration control in smart structures is an area of interest, mainly due to the demand for better performance of mechanical systems, such as aircraft and aerospace structures. Smart structures, formed using actuators and sensors, can improve the dynamic performance with the application of several kinds of controllers. This article describes the application of a technique based on linear matrix inequalities (LMI) to design an active control system. The positioning of the actuators, the design of a robust state feedback controller and the design of an observer are all achieved using LMI. The following are considered in the controller design: limited actuator input, bounded output (energy) and robustness to parametric uncertainties. Active vibration control of a flat plate is chosen as an application example. The model is identified using experimental data by an eigensystem realization algorithm (ERA) and the placement of the two piezoelectric actuators and single sensor is determined using a finite element model (FEM) and an optimization procedure. A robust controller for active damping is designed using an LMI framework, and a reduced model with observation and control spillover effects is implemented using a computer. The simulation results demonstrate the efficacy of the approach, and show that the control system increases the damping in some of the modes.

Active flutter suppression in a 2-D airfoil using linear matrix inequalities techniques

Flutter is an in-flight vibration of flexible structures caused by energy in the airstream absorbed by the lifting surface. This aeroelastic phenomenon is a problem of considerable interest in the aeronautic industry, because flutter is a potentially destructive instability resulting from an interaction between aerodynamic, inertial, and elastic forces. To overcome this effect, it is possible to use passive or active methodologies, but passive control adds mass to the structure and it is, therefore, undesirable. Thus, in this paper, the goal is to use linear matrix inequalities (LMIs) techniques to design an active state-feedback control to suppress flutter. Due to unmeasurable aerodynamic-lag states, one needs to use a dynamic observer. So, LMIs also were applied to design a state-estimator. The simulated model consists of a classical flat plate in a two-dimensional flow. Two regulators were designed, the first one is a non-robust design for parametric variation and the second one is a robust control design, both designed by using LMIs. The parametric uncertainties are modeled through polytopic uncertainties. The paper concludes with numerical simulations for each controller. The open-loop and closed-loop responses are also compared and the results show the flutter suppression. The perfomance for both controllers are compared and discussed. Keywords : Flutter, active control, LMI, polytopic uncertainties, robustness

Vibration energy harvesting using piezoelectric transducer and non-controlled rectifiers circuits

Vibration energy harvesting with piezoelectric materials is of practical interest because of the demand for wireless sensing devices and low-power portable electronics without external power supply. For practical use of vibration energy harvester with piezoelectric materials, it is necessary to process the alternating current (AC) by using different rectifiers circuits in order to charge batteries with direct current (DC) or to feed electronic devices. Unfortunately, most of the models used focused on simplifying the energy harvesting circuit into a simple resistive load. In the real-world applications, the energy harvesting external circuit is more complex than a simple load resistance. In this sense, the goal of the present paper is to describe a comprehensive strategy for power harvesting device to estimate the output power provided by a cantilever beam with the electrodes of the piezoceramic layers connected to a standard rectifier circuit. The true electrical components were considered in the full-wave rectifier circuit with four diodes in bridge. A very simple and comprehensive description for choosing the capacitance and resistance loads is provided. In order to illustrate the results, numerical simulations and experimental verifications are also performed to ensure the accuracy. All tests and results are described and detailed using Matlab, the SimPowerSystem toolbox of the Simulink and an experimental setup.

On the application of discrete-time Volterra series for the damage detection problem in initially nonlinear systems

Nonlinearities in the dynamical behavior of mechanical systems can degrade the performance of damage detection features based on a linearity assumption. In this article, a discrete Volterra model is used to monitor the prediction error of a reference model representing the healthy structure. This kind of model can separate the linear and nonlinear components of the response of a system. This property of the model is used to compare the consequences of assuming a nonlinear model during the nonlinear regime of a magneto-elastic system. Hypothesis tests are then employed to detect variations in the statistical properties of the damage features. After these analyses, conclusions are made about the application of Volterra series in damage detection.

Damage detection in nonlinear structures using discrete-time volterra series

Structural damage identification is basically a nonlinear phenomenon; however, nonlinearprocedures are not used currently in practical applications due to the complexity and difficulty forimplementation of such techniques. Therefore, the development of techniques that consider the nonlinearbehavior of structures for damage detection is a research of major importance since nonlineardynamical effects can be erroneously treated as damage in the structure by classical metrics. Thispaper proposes the discrete-time Volterra series for modeling the nonlinear convolution between theinput and output signals in a benchmark nonlinear system. The prediction error of the model in anunknown structural condition is compared with the values of the reference structure in healthy conditionfor evaluating the method of damage detection. Since the Volterra series separate the responseof the system in linear and nonlinear contributions, these indexes are used to show the importanceof considering the nonlinear behavior of the structure. The paper concludes pointing out the mainadvantages and drawbacks of this damage detection methodology.

Adaptive filter feature identification for structural health monitoring in an aeronautical panel

This article presents an approach to structural health monitoring (SHM) using adaptive filters. The experimental signals from different structural conditions provided by piezoelectric actuators/sensors bonded in the test structure are modeled by a discrete-time recursive least square (RLS) filter. The biggest advantage of using a RLS filter is the clear possibility to perform an online SHM procedure because the identification is also valid for nonstationary linear systems. An online damage-sensitive index feature is computed based on portions of the autoregressive coefficients normalized by the square root of the sum of the squares. The proposed method is then used in a laboratory test involving an aeronautical panel coupled with piezoelectric sensors/actuators (PZTs) in different positions. To test this hypothesis, the t-test is used to obtain the damage decision. The proposed algorithm was able to identify and localize damages in the structure. The article concludes by exploring the applicability and drawbacks of the method and proposes some implementation suggestions.

UPDATING OF A NONLINEAR FINITE ELEMENT MODEL USING DISCRETE-TIME VOLTERRA SERIES

IN THIS STUDY, THE DISCRETE-TIME VOLTERRA SERIES ARE USED TO UPDATE PARAMETERS IN A NONLINEAR FINITE ELEMENT MODEL. THE MAIN IDEA OF THE VOLTERRA SERIES IS TO DESCRIBE THE DISCRETE-TIME OUTPUT OF A NONLINEAR SYSTEM USING MULTIDIMENSIONAL CONVOLUTIONS BETWEEN THE VOLTERRA KERNELS REPRESENTED IN A KAUTZ ORTHOGONAL BASIS AND THE EXCITATIONS. A METRIC BASED ON THE RESIDUE BETWEEN THE EXPERIMENTAL AND THE NUMERICAL VOLTERRA KERNELS IS USED TO IDENTIFY THE PARAMETERS OF THE NUMERICAL MODEL. FIRST, THE IDENTIFICATION OF THE LINEAR PARAMETERS IS PERFORMED USING A METRIC BASED ONLY ON THE FIRST ORDER VOLTERRA KERNELS. THEN THE NONLINEAR PARAMETERS ARE IDENTIFIED THROUGH A METRIC BASED ON THE HIGHER-ORDER KERNELS. THE ORIGINALITY OF THIS NONLINEAR UPDATING METHOD STEMS FROM THE DECOUPLING OF LINEAR AND NONLINEAR PARAMETERS AND THE USE OF GLOBAL NONLINEAR MODEL. IN ORDER TO PUT IN LIGHT THE APPLICABILITY OF THIS TECHNIQUE, THIS WORK FOCUS ON THE IDENTIFICATION OF THE PARAMETERS IN A NONLINEAR FINITE ELEMENT MODEL OF A BEAM THAT WAS PRELOADED BY COMPRESSION MECHANISM. THIS WORK SHOWS THAT THE UPDATED NUMERICAL MODEL WAS ABLE TO REPRESENT THE BEHAVIOUR OBSERVED IN THE EXPERIMENTAL MEASUREMENTS.