A novel time-frequency procedure is presented in this paper for guided wave (GW) propagation analysis in structure
health monitoring (SHM) applications.The proposed approach combines the Warped Frequency Transform
(WFT) with a Basis Pursuit algorithm to generate a sparse yet accurate time-frequency representation of the
acquired signals even in the case of multi-modal dispersive propagation associated to broadband excitation of
the waveguide. This is obtained through over complete dictionaries composed by optimized atoms which are
designed to match the spectro-temporal structure of the various propagating modes.The Warped Basis Pursuit
(W-BP) decomposition of several acquired waveforms results in distance signals that can be combined through
classical beamforming techniques for imaging purposes.
This approach is tested on experimental data obtained by broadband GW excitation in a 1 mm thick aluminum
plate with an artificially introduced through crack, followed by multiple waveguide displacement recording through a scanning laser Doppler vibrometer. Dispersion compensation and high-resolution source as well as defect imaging is demonstrated even in domain regions that are not directly accessible for measurement.
The development of effective damage imaging and characterization tools is a challenging task because of the
dispersive and multi-modal nature of Lamb waves. An additional problem is the need for baseline data that is
required by a number of existing techniques. This paper presents the development of imaging algorithms applied
to filtered wavefield data received from piezoelectric disc sources. Frequency-wavenumber filtering is used to
separate incident/backscattered waves and individual wave modes. Filtered data are provided as input to imaging algorithms that detect damage and estimate its location. The implementation of incident and backscattered waves separation procedures avoids the need for a baseline, while mode separation permits the analysis of modes that are most sensitive to damage. The proposed algorithm is verified experimentally for damage detection on an aluminum plate.
KEYWORDS: Protactinium, Transducers, Microsoft Foundation Class Library, Actuators, Solids, Composites, Fourier transforms, Wave plates, Interfaces, Chemical elements
The paper describes a numerical approach for the analysis of Lamb wave generation in plate structures. Focus
is placed on the investigation of macro fiber composite (MFC) actuators and their directivity properties when
actuated individually. A local Finite Element model of the electro-mechanical behavior of the actuator/substrate
system estimates the distribution of the interface stresses between the actuator and the substrate, which are
subsequently provided as inputs to the analytical procedure that estimates the far-field response of the plate.
The proposed approach allows handling of complex actuation configurations, as well as the presence of a bonding layer. As an example, the technique is applied to estimate the directional Lamb wave generation of two types of macro fiber composite transducers. The numerical results are validated experimentally by using a Polytec PSV400 MS scanning laser doppler vibrometer. The results suggest the potentials of the approach as a tool for the prediction of the excitation provided by actuators of complex shapes.
Lamb-wave testing for structural health monitoring is complicated by the dispersion nature of the wave
modes. The dispersion effect will result in a propagated wave with longer time duration, deformed
envelop shape as compared to its excitation counterpart, and hard to be interpreted. This paper first
reviews the dispersion compensation and removal algorithms. Second, it compares these two methods
by applying them to two widely used low-frequency Lamb wave modes: S0 and A0. Numerical
simulations are compared in parallel with experimental results. Finally, the dispersion compensation
algorithm is applied to 1-D PWAS phased array and demonstrated to improve the phase array's spatial
resolution.
Matching pursuit (MP) is an adaptive signal decomposition technique and can be applied to process
Lamb waves, such as denoising, wave parameter estimation, and feature extraction, for health
monitoring applications. This paper explored matching pursuit decomposition using Gaussian and
chirplet dictionaries to decompose/approximate Lamb waves and extract wave parameters. While
Gaussian dictionary based MP is optimal for decomposing symmetric signals, chirplet dictionary
based MP is able to decompose asymmetric signals, e.g., dispersed Lamb wave. The extracted
parameter, chirp rate, from the chirplet MP can be used to correlate with two Lamb wave modes, S0
and A0.
Lamb wave time reversal method is a new and tempting baseline-free damage detection technique for structural
health monitoring. With this method, certain types of damage can be detected without baseline data. However, the
application of this method to thin-wall structures is complicated by the existence of at least two Lamb wave modes
at any given frequency, and by the dispersion nature of the Lamb wave modes existing in thin-wall structures. The
theory of Lamb wave time reversal has not yet been fully studied.
This paper addresses this problem by developing a theoretical model for the analysis of Lamb wave time
reversal in thin-wall structures based on the exact solutions of the Rayleigh-Lamb wave equation. The theoretical
model first used to predict the existence of single-mode Lamb waves. Then the time reversal behavior of single-mode
and two-mode Lamb waves is studied numerically. The advantages of single-mode tuning in the application
of time reversal damage detection are highlighted. The validity of the proposed theoretical model is verified through
experimental studies. In addition, a similarity metric for judging time invariance of Lamb wave time reversal is
presented. It is shown that, under certain condition, the use of PWAS-tuned single-mode Lamb waves can greatly
improve the effectiveness of the time-reversal damage detection procedure.
KEYWORDS: Digital signal processing, Structural health monitoring, Data acquisition, Field programmable gate arrays, Analog electronics, Bandpass filters, Linear filtering, Clocks, Signal generators, Smoothing
The electromechanical (E/M) impedance method is a powerful technique in active structural health monitoring (SHM). E/M impedance method utilizes as its main apparatus an impedance analyzer that reads the in-situ E/M impedance of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. Present-day impedance analyzer equipments (e.g.HP4194) are bulky, heavy and expensive laboratory equipment that cannot be carried into the field for on-site structural health monitoring. This paper presented the development of a compact and low-cost impedance analyzer system. First, two types of impedance measurement approaches were evaluated in a PC-based simplified impedance analyzer system. It was found that the first approach, which measures impedance frequency by frequency, is very accurate but is not time-efficient and needs more efforts to be implemented. As for the second approach, which measures impedance using broad-band excitation and transfer function method, provides a good compromise among the measurement time-efficient, accuracy and implementation efforts. Experimental results show that this approach can be used for E/M impedance method for structural health monitoring. Second, to eliminate the PC in the measurement system, a DSP-based impedance analyzer system is proposed for further miniaturization. The system hardware configuration and design, software state flow for impedance measurement, and preliminary testing were presented in details.
Two types of piezoelectric wafer active sensor (PWAS) oscillators, Colpitts-type PWAS oscillator and series-type PWAS oscillator, designed for in-vivo monitoring of capsule formation around soft tissue implants have been presented. Both of the oscillators were explored analytically and experimentally. Colpitts-type PWAS oscillator uses the inductive property of the PWAS in its resonant frequency range and operates at the first resonant frequency of the PWAS. However, it is too sensitive to the surrounding damping. Therefore, it may not be an appropriate candidate for in-vivo application. For the series-type PWAS oscillator, some preliminary experiments showed that this type of oscillator can tell the difference in viscosity/damping conditions of different media. More work, such as calibration of output of the oscillator, needs to be done before using it in in-vivo monitoring of capsule formation.
KEYWORDS: Structural health monitoring, Digital signal processing, Signal processing, Data acquisition, Active sensors, Fourier transforms, Analytical research, Signal generators, Interference (communication), Semiconducting wafers
Electro-mechanical impedance method is emerging as an important and powerful technique for structural health monitoring. The E/M impedance method utilizes as its main apparatus an impedance analyzer that reads the in-situ E/M impedance of the piezoelectric wafer active sensors (PWAS) attached to the monitored structure. Present-day impedance analyzer equipments (e.g. HP4194) are bulky, heavy and expensive laboratory equipment that cannot be carried into the field for on-site structural health monitoring. To address this issue, several investigators have explored means of miniaturizing the impedance analyzer making the impedance analyzer more compact and field-portable.
In this paper we present an improved algorithm for efficient measurement of the E/M impedance using PWAS transducers. Instead of using a sine wave as the excitation signal to the PWAS and slowly changing its frequency, our method utilizes a chirp signal which is abundant in frequency components. By applying Fast Fourier Transform (FFT) to both the input and response signals, the impedance spectrum of the PWAS is acquired. The algorithm was implemented and tested in a real-time system, which consists of excitation signal generation module, voltage and current measurement module and digital signal acquisition module. The size and the implementation of the overall system using either a laptop or a digital signal processor (DSP) are also discussed. Finally, practical results are presented and comparatively examined.
KEYWORDS: Structural health monitoring, Signal processing, Digital signal processing, Fourier transforms, Data acquisition, Interference (communication), Software development, Resistors, Semiconducting wafers, Active sensors
Electromechanical (E/M) impedance method is emerging as an effective and powerful technique for structural health monitoring. The E/M impedance method utilizes as its main apparatus an impedance analyzer that reads the in-situ E/M impedance of piezoelectric wafer active sensors (PWAS) attached to the monitored structure. Laboratory-type impedance analyzers (e.g. HP4194) are bulky, heavy, and expensive. They cannot be easily carried into the field for on-site structural health monitoring. To address this issue, means of to reduce the size of the impedance analyzer making the impedance analyzer more compact and field-portable are explored. In this paper, we present a systematic approach to the development of a field-portable small-size impedance analyzer for structural health monitoring using the electromechanical impedance technique. Our approach consists of several developmental stages. First, we perform a simulation of the E/M Impedance technique and develop the software tools for analyzing the signal in a fast and efficient way while maintaining the desired accuracy. The objective of this signal processing part is to obtain the complex impedance, ZR+iZI)=|Z| angle arg Z, at a number of frequencies in a predetermined range. Several signal processing methods were explored such as: (a) integration method; (b) correlation method; (c) Discrete Fourier transform (DFT) method. Second, we discuss the hardware issues associated with the implementation of this approach. The hardware system architecture consists of several blocks: (a) reference signal generation; (b) voltage and current measurements; and (c) digital signal acquisition and processing. Practical results obtained during proof-of-concept experiments are presented and comparatively examined.
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