Zhongqing Su

A damage identification technique for CF/EP composite laminates using distributed piezoelectric transducers

Abstract In this study, a damage identification approach was developed for carbon fibre/epoxy composite laminates with localized internal delamination. Propagation of the Lamb wave in laminates and its interaction with the delamination were examined. The fundamental symmetric Lamb wave mode, S 0 , and the lowest order shear wave mode, S 0 ′ , were chosen to predict damage location. A real-time active diagnosis system was therefore established. This technique uses distributed piezoelectric transducers to generate and monitor the ultrasonic Lamb wave with narrowband frequency. The two-way switches were employed to minimize the number of transducers. A signal-processing scheme based on the time–frequency spectrographic analysis was utilised to extract useful diagnostic information. Also, an optimal identification method was applied on damage searching procedure to reduce errors and obtain the diagnostic results promptly. Experiments were conducted on [0/−45/45/90] s CF/EP laminates to verify this diagnosis system. The results obtained show that satisfactory detection accuracy could be achieved.

Crack identification in aluminium plates using Lamb wave signals of a PZT sensor network

With an integrated active piezoelectric sensor network, a Lamb wave-based crack identification technique for aluminium plates was developed. Experimental results showed that the propagation of Lamb waves in aluminium plate-like structures is considerably complicated due to wave dispersion, material attenuation, boundary reflection, etc. In order to eliminate the diverse interference, a wavelet transform technique was applied to purify the acquired Lamb wave signals, and the characteristics of Lamb wave signals were extracted from the wave energy spectrum. A correlation function was further established, which helped identify the crack position based on a triangulation approach with the aid of a nonlinear least-squares optimization algorithm. Such an approach provides satisfactory results in locating the crack position in aluminium plates with cracks of 5 and 20 mm in length.

A built-in active sensor network for health monitoring of composite structures

An embedded sensor network technique was developed for improving the overall integrity of functionalized composite structures engaged in aircraft. A set of miniaturized piezoelectric wafers was designed and circuited to configure a built-in active actuator/sensor network, which was immobilized into multi-layered composite laminates. The propagation characteristics of Lamb waves generated and collected by this built-in sensor network in carbon fibre-reinforced composite laminates were investigated. The influence of a stiffener and of the excitation frequency on the propagation of the Lamb waves generated was evaluated. A study was carried out to assess delamination in CF/EP (carbon fibre/epoxy) woven laminates, by fusing information from multiple sensing paths of the embedded network on the basis of the Hilbert transform, signal correlation and probabilistic searching. An excellent identification capability indicates the considerable application potential of the proposed sensor network approach in providing high-fidelity data acquisition and condition monitoring for composite aircraft structures.

Probabilistic Damage Identification Based on Correlation Analysis Using Guided Wave Signals in Aluminum Plates

An algorithm based on correlation analysis was adopted to estimate the probability of the presence of damage in aluminum plates using Lamb wave signals from an active sensor network. Both finite element analysis and experimental evaluations were presented. The Shannon entropy optimization criterion was applied to calibrate the optimal mother wavelet and the most appropriate continuous wavelet transform scale for signal processing. The correlation coefficients for individual sensing paths between the present state (with damage) and the reference state (without damage) were calculated, and the probability of the presence of damage in the monitoring area enclosed by the active sensor network was estimated to identify the damage. A concept of virtual sensing paths (VSPs) was proposed to enhance the performance of the algorithm by increasing the number of sensing paths in data fusion. The results identified using both simulation and experimental Lamb wave signals from different groups of sensing paths at diffe...Dong Wang, Lin Ye,* Zhongqing Su, Ye Lu, Fucai Li and Guang Meng Laboratory of Smart Materials and Structures (LSMS), Centre for Advanced Materials Technology (CAMT), School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, NSW 2006, Australia The Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong Kong SAR, People’s Republic of China State Key Laboratory of Mechanical System and Vibration, Shanghai Jiao Tong University, 1954 Huashan road, Shanghai 200030, People’s Republic of China

Lamb wave propagation-based damage identification for quasi-isotropic CF/EP composite laminates using artificial neural algorithm: Part I - Methodology and database development

A guided Lamb wave-based damage identification scheme and an online structural health monitoring (online-SHM) system with an integrated piezoelectric actuator-sensor network are developed. The proposed methodology is applied to the quantitative diagnosis of through-hole-type defect in the CF-EP quasi-isotropic laminate with the aid of an artificial neural network algorithm. For this purpose, a variety of composite laminates with stochastic damages are considered, and the corresponding three-dimensional dynamic FEM simulations are conducted. To describe a Lamb wave excited by the PZT actuator, models for both the piezoelectric actuator and sensor coupled with the composite laminates are established. A wavelet transform-based signal processing package (SPP) is devised to purify the acquired wave signals, and further extract characteristics from the energy spectra of Lamb waves over the time-scale domain. A concept of ‘digital damage fingerprints’ is introduced, with which a damage parameters database (DPD) is constructed and used to offline train a multilayer feedforward neural network, supervised by an error-back propagation (BP) neural algorithm. Such an identification technique is then validated, to be described in the second part of this study.

Damage Identification of Metallic Structures Using A0 Mode of Lamb Waves

A Lamb wave-based technique was developed for detecting damages in metallic structures, such as cracks and holes in metallic beams and plates. For metallic structures with transverse cracks and holes, A0 mode of Lamb waves was employed due to its shorter wave length compared with S0 mode, which leads to high sensitivity to small damages. Two kinds of excitation techniques for generating comparatively pure A0 mode using piezoelectric lead zirconate titanate (PZT) actuators were realized experimentally. In one technique, two PZT actuators with applied out-of-phase voltages were attached on both sides of the structures. While in the other technique, a kind of grease lubricant was used between the bottom surface of one PZT actuator and the surface of the specimens. Both techniques were able to enhance the component of A0 mode and reduce the component of S0 mode effectively. Secondly, in terms of the arrival time of the A0 wave mode reflected from damages obtained using the wavelet analysis, the positions of damages were identified accurately. The above two techniques were then validated by identifying the transverse cracks and holes in aluminum beams and plates, respectively. Numerical simulations using the finite element method (FEM) for the wave propagation in these structures with damages were carried out. The obtained experimental and numerical results demonstrate that it is possible to identify damage position very accurately by using only sensor data of defective structures without referring benchmark signals (sensor data of intact structures).

Fundamental Lamb Mode-based Delamination Detection for CF/EP Composite Laminates Using Distributed Piezoelectrics

A delamination detection scheme for the CF/EP composite laminates based on the Lamb wave propagation was proposed. The fundamental symmetric Lamb mode (S 0) and the delaminationinduced basic shear mode (S’0) in an ultrasonic frequency range were utilised for locating the delamination. Both numerical simulation and experimental studies were performed to evaluate the propagation characteristics of Lamb waves in the composite laminates involving a delamination. An active online damage diagnosis system was established with a transducer network configured using distributed piezoelectrics. Bandpass filters were designed and spectrographic analyses in the time-scale space via wavelet transform technique were accomplished, to diminish diverse broadband interferences and consequently improve signal interpretation. Algorithm based on a graphic approach was introduced into the damage searching procedure to expedite the diagnosis and minimise the estimation errors. The proposed identification scheme and diagnosis system were then validated by detecting delaminations in CF/EP laminates made from unidirectional and woven fabric prepreg, respectively. Satisfactory prediction for the damage location has been achieved. Additionally, the influence of diagnostic waveforms and frequencies on the identification accuracy was also evaluated.

Predicting delamination of composite laminates using an imaging approach

The present work concerns the development of a Lamb-wave-based imaging approach with the capacity to visually pinpoint structural damage, if any, in terms of the probability of damage occurrence at all spatial positions of the structure under inspection. To establish such probabilities, individual sensors of an active sensor network contributed their perceptions as to the damage occurrence near them using the signal feature time-of-flight (ToF) extracted from captured Lamb wave signals. All these perceptions were then fused by virtue of an image arithmetic algorithm. The prediction results were presented in an image where the location and size of all the damage instances in the structure became intuitional, rather than provided with definitive damage parameters. Such a probability-based imaging approach is by nature more consistent with the implication of prediction or estimation of damage than traditional identification endeavours. The effectiveness of the approach was experimentally demonstrated by predicting delamination in carbon-fibre-reinforced epoxy (CF/EP) laminates.

On Selection of Data Fusion Schemes for Structural Damage Evaluation

Zhongqing Su,* Xiaoming Wang, Li Cheng, Long Yu and Zhiping Chen Department of Mechanical Engineering, The Hong Kong Polytechnic University Kowloon, Hong Kong SAR Urban Systems Program, CSIRO Sustainable Ecosystems, Commonwealth Scientific and Industrial Research Organisation, 37 Graham Road, Highett Melbourne VIC 3190, Australia Boeing Hawker de Havilland, 226 Lorimer Street, Fishermans Bend VIC 3207, AustraliaData fusion plays a pivotal role to achieve reasonable accuracy and precision in identifying structural damage. An appropriate fusion process can reduce imprecision, uncertainties and incompleteness, therefore increasing the robustness and reliability of identification. The present work compared three major fusion schemes, i.e., disjunctive, conjunctive, and compromise fusion, in terms of their effectiveness to estimate mono- and multi-delamination in carbon fiber-epoxy composite structures. (1) Time-of-flight was extracted from Lamb wave signals rendered by an active sensor network, to attain the loci of locations of all possible damage instance(s) in the structure under inspection, which served as the prior perceptions of sensors as to the areas with possibility of damage occurrence; and (2) the entire structure was virtually meshed and the prior perceptions of individual sensors were further quantified at each spatial mesh node using the distance between nodes and all loci established from (1), to form...

Evaluation of fatigue cracks using nonlinearities of acousto-ultrasonic waves acquired by an active sensor network

There has been increasing interest in using the nonlinear features of acousto-ultrasonic (AU) waves to detect damage onset (e.g., micro-fatigue cracks) due to their high sensitivity to damage with small dimensions. However, most existing approaches are able to infer the existence of fatigue damage qualitatively, but fail to further ascertain its location and severity. A damage characterization approach, in conjunction with the use of an active piezoelectric sensor network, was established, capable of evaluating fatigue cracks in a quantitative manner (including the co-presence of multiple fatigue cracks, and their individual locations and severities). Fundamental investigations, using both experiment and enhanced finite element analysis dedicated to the simulation of nonlinear AU waves, were carried out to link the accumulation of nonlinearities extracted from high-order AU waves to the characteristic parameters of a fatigue crack. A probability-based diagnostic imaging algorithm was developed, facilitating an intuitive presentation of identification results in images. The approach was verified experimentally by evaluating multi-fatigue cracks near rivet holes of a fatigued aluminum plate, showing satisfactory precision in characterizing real, barely visible fatigue cracks. Compared with existing methods, this approach innovatively (i) uses permanently integrated active sensor networks, conducive to automatic and online health monitoring; (ii) characterizes fatigue cracks at a quantitative level; (iii) allows detection of multiple fatigue cracks; and (iv) visualizes identification results in intuitive images.