Multiple damage identification and imaging in an aluminum plate using effective Lamb wave response automatic extraction technology

2016 ◽  
Author(s):  
Qinghua Ouyang ◽  
Li Zhou ◽  
Xiaotong Liu
Keyword(s):  
Lamb Wave ◽  
Damage Identification ◽  
Aluminum Plate ◽  
Automatic Extraction ◽  
Extraction Technology ◽  
Wave Response

Structural Health Monitoring of Thin Aluminum Plate Using Acoustic Sensors

2012 ◽  
Vol 622-623 ◽  
pp. 1389-1395
Author(s):  
R. Nishanth ◽  
K. Lingadurai ◽  
V. Malolan ◽  
Gowrishankar Wuriti ◽  
M.R.M. Babu
Keyword(s):  
Lamb Wave ◽  
Damage Identification ◽  
Lamb Waves ◽  
Time Domain Analysis ◽  
Ultrasonic Waves ◽  
Aluminum Plate ◽  
Identification System ◽  
Time Frequency ◽  
Thin Aluminum ◽  
Aluminum Plates

SHM is defined as “an emerging technology that can be defined as continuous, autonomous, real time, in-service monitoring of the physical condition of a structure by means of embedded or attached sensors with minimum manual intervention” .SHM provides the ability of a system to detect adverse changes within a system’s structure to enhance reliability and reduce maintenance costs. There are different Non-Destructive techniques like acoustic emission, ultrasonic, acousto-ultrasonic, guided ultrasonic waves or Lamb waves which are nowadays investigated for the development of an efficient and user-friendly damage identification system. This paper deals with the latter which is based on Lamb wave propagation. It has been developed especially for distinguishing different kinds of damages. The Lamb wave-based active SHM method uses piezoelectric (PZT) sensors to transmit and receive Lamb waves in a thin Aluminum plate. The Lamb wave modes (AO &SO) travel into the structure and are reflected by the structural boundaries, discontinuities, and damage. By studying their propagation and reflection, the presence of defect in the structure is determined. Laboratory level experiments have been carried out on thin Aluminum plates with angular, horizontal and vertical defect. The obtained waveform is filtered to avoid unwanted noise & disturbances using Savitzky-Golay filtering. The filtered waveforms are compared to differentiate the defects. Short Time Fourier Transform has been carried out on the acquired waveform. This study provides significant insight into the problem of identifying localized damages in the structure using PZT and dispersion of signal after they interact with different types of damage. Those small defects like the horizontal one that may be nearly missed in time domain analysis can also be clearly identified in the STFT analysis. Moreover the occurrence of So mode is also clearly seen. Thus, Lamb waves generated by PZT sensors and time-frequency analysis techniques could be used effectively for damage detection in aluminum plate. This study has given a complete idea of the working and the basic requirements of SHM system.

Simulation Study of Damage Identification Method Based on Lamb Wave Scattering in Aluminium Plate

2012 ◽  
Vol 433-440 ◽  
pp. 2611-2618
Author(s):  
Zhen Hua Tian ◽  
Hong Yuan Li ◽  
Hong Xu
Keyword(s):  
Energy Distribution ◽  
Lamb Wave ◽  
Damage Identification ◽  
Optimization Method ◽  
Propagation Path ◽  
Transient Dynamic Analysis ◽  
Time Frequency ◽  
Damage Location ◽  
Identification Method ◽  
Short Time

The propagation of scattering Lamb wave in plate was simulated using transient dynamic analysis in ANSYS. In order to extract the characteristic information of received signal for damage identification, the short time Fourier transform based on time-frequency analysis was utilized, and then the energy distribution and envelop of received signal were obtained. Based on the displacement contour of simulation and energy distribution, the propagation of scattering wave in plate with a through hole was examined. Also, a mathematic relationship between damage location and scattering signal was developed, with the help of wave propagation path through actuator, damage and sensor. A nonlinear optimization method was applied on the mathematic relationship to obtain the damage location. The damage identification method using scattering Lamb wave was therefore established.

scholarly journals Damage Identification of Large Generator Stator Insulation Based on PZT Sensor Systems and Hybrid Features of Lamb Waves

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2745 ◽  
Author(s):  
Ruihua Li ◽  
Hao Li ◽  
Bo Hu
Keyword(s):  
Power Systems ◽  
Lamb Wave ◽  
Damage Identification ◽  
Lamb Waves ◽  
Fem Simulation ◽  
Correlation Coefficients ◽  
Hybrid Features ◽  
Material Inhomogeneity ◽  
Time Frequency ◽  
Damage Imaging

Large generators are the principal pieces of equipment in power systems, and their operation reliability critically depends on the stator insulation. Damages in stator insulation will gradually lead to the failure and breakdown of generator. Due to the advantages of Lamb waves in Structural health monitoring (SHM), in this study, a distributed piezoelectric (PZT) sensor system and hybrid features of the Lamb waves are introduced to identify stator insulation damage of large generator. A hierarchical probability damage-imaging (PDI) algorithm is proposed to tackle the material inhomogeneity and anisotropy of the stator insulation. The proposed method includes three steps: global detection using correlation coefficients, local detection using Time of flight (ToF) along with the amplitude of damage-scattered Lamb wave, and final images fusion. Wavelet Transform was used to extract the ToF of Lamb wave in terms of the time-frequency domain. Finite Element Modeling (FEM) simulation and experimental work were carried out to identify four typical stator insulation damages for validation, including inner void, inner delamination, puncture, and crack. Results show that the proposed method can precisely identify the location of stator insulation damage, and the reconstruction image can be used to identify the size of stator insulation damage.

scholarly journals A Damage Detection Method Using Neural Network Optimized by Multiple Particle Collision Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Sergio V. Farias ◽  
Osamu Saotome ◽  
Haroldo F. Campos Velho ◽  
Elcio H. Shiguemori
Keyword(s):  
Neural Network ◽  
Damage Detection ◽  
Lamb Wave ◽  
Learning Strategy ◽  
Damage Identification ◽  
Particle Collision ◽  
Learning Approaches ◽  
Propagation Methods ◽  
Artificial Neural Network Ann ◽  
Multiple Particle

A critical task of structural health monitoring is damage detection and localization. Lamb wave propagation methods have been successfully applied for damage identification in plate-like structures. However, Lamb wave processing is still a challenging task due to its multimodal and dispersive characteristics. To address this issue, data-driven machine learning approaches as artificial neural network (ANN) have been proposed. However, the effectiveness of ANN can be improved based on its architecture and the learning strategy employed to train it. The present paper proposes a Multiple Particle Collision Algorithm (MPCA) to design an optimum ANN architecture to detect and locate damages in plate-like structures. For the first time in the literature, the MPCA is applied to find damages in plate-like structures. The present work uses one piezoelectric transducer to generate Lamb wave signals on an aluminum plate structure and a linear array of four transducers to capture the scattered signals. The continuous wavelet transform (CWT) processes the captured signals to estimate the time-of-flight (ToF) that is the ANN inputs. The ANN output is the damage spatial coordinates. In addition to MPCA optimization, this paper uses a quantitative entropy-based criterion to find the best mother wavelet and the scale values. The presented experimental results show that MPCA is capable of finding a simple ANN architecture with good generalization performance in the proposed damage localization application. The proposed method is compared with the 1-dimensional convolutional neural network (1D-CNN). A discussion about the advantages and limitations of the proposed method is presented.

Non-model-based damage identification of plates using measured mode shapes

2016 ◽  
Vol 16 (1) ◽  
pp. 3-23 ◽  
Author(s):  
Yongfeng Xu ◽  
Weidong Zhu
Keyword(s):  
Structural Damage ◽  
Damage Identification ◽  
Damage Index ◽  
Thickness Reduction ◽  
Aluminum Plate ◽  
Mode Shapes ◽  
Identification Method ◽  
Model Based

Mode shapes (MSs) have been extensively used to detect structural damage. This paper presents a new non-model-based damage identification method that uses measured MSs to identify damage in plates. A MS damage index (MSDI) is proposed to identify damage near regions with consistently high values of MSDIs associated with MSs of different modes. A MS of a pseudo-undamaged plate can be constructed for damage identification using a polynomial of a properly determined order that fits the corresponding MS of a damaged plate, if the associated undamaged plate is geometrically smooth and made of materials that have no stiffness and mass discontinuities. It is shown that comparing a MS of a damaged plate with that of a pseudo-undamaged plate is better for damage identification than with that of an undamaged plate. Effectiveness and robustness of the proposed method for identifying damage of different positions and areas are numerically investigated using different MSs; effects of crucial factors that determine effectiveness of the proposed method are also numerically investigated. Damage in the form of a machined thickness reduction area was introduced to an aluminum plate; it was successfully identified by the proposed method using measured MSs of the damaged plate.

scholarly journals Multi-feature Fusion and Damage Identification of Large Generator Stator Insulation Based on Lamb Wave Detection and SVM Method

Sensors ◽  
2019 ◽  
Vol 19 (17) ◽  
pp. 3733
Author(s):  
Ruihua Li ◽  
Haojie Gu ◽  
Bo Hu ◽  
Zhifeng She
Keyword(s):  
Fractal Dimension ◽  
Lamb Wave ◽  
Damage Identification ◽  
Feature Fusion ◽  
Support Vector ◽  
Detection And Identification ◽  
Condition Evaluation ◽  
Power Spectral ◽  
Single Feature ◽  
Generator Stator

Due to the merits of Lamb wave to Structural Health Monitoring (SHM) of composite, the Lamb wave-based damage detection and identification technology show a potential solution for the insulation condition evaluation of large generator stator. This was performed in order to overcome the problem that it is difficult to effectively identify the stator insulation damage the using single feature of Lamb wave. In this paper, a damage identification method of stator insulation based on Lamb wave multi-feature fusion is presented. Firstly, the different damage features were extracted from time domain, frequency domain, and fractal dimension of lamb wave signals, respectively. The features of Lamb wave signals were extracted by Hilbert transform (HT), power spectral density (PSD), fast Fourier transform (FFT), and wavelet fractal dimension (WFD). Then, a machine learning method based on support vector machine (SVM) was used to fuse and reconstruct the multi-features of Lamb wave and furtherly identify damage type of stator insulation. Finally, the effect of typical stator insulation damage identification is verified by simulation and experiment.

A Bayesian fusion method for composite damage identification using Lamb wave

2020 ◽  
pp. 147592172094500
Author(s):  
Haode Huo ◽  
Jingjing He ◽  
Xuefei Guan
Keyword(s):  
Lamb Wave ◽  
Composite Plate ◽  
Damage Identification ◽  
Reconstruction Algorithm ◽  
Damage Location ◽  
Numerical Studies ◽  
Composite Damage ◽  
Bayesian Fusion ◽  
Novel Method ◽  
Reliability And Robustness

This study presents a novel method for composite damage identification using Lamb wave. A probabilistic integration of the elliptical loci method and the RAPID (reconstruction algorithm for probabilistic inspection of defects) in a Bayesian framework is proposed. The proposed method allows for the incorporation of multiple damage sensitive features in a rational manner to improve the reliability and robustness for a given array of sensors. Numerical studies are performed to verify the effectiveness of the proposed method and to compare its accuracy with existing methods. Experimental investigation using a realistic composite plate is made to further validate the proposed method. The influence of damage location and the number of participating sensors on the performance of the proposed method is discussed. Results indicate that the proposed method yields more accurate and reliable results comparing with existing methods.

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