Fractal Theory Based Damage Assessing Method of Acoustic Emission Test

2009 ◽  
Vol 413-414 ◽  
pp. 335-342 ◽  
Author(s):  
Yong Huang ◽  
Hui Li ◽  
Xin Yan ◽  
Jin Ping Ou
Keyword(s):  
Acoustic Emission ◽  
Development Process ◽  
Fractal Theory ◽  
Damage Index ◽  
Damage Development ◽  
Ae Signal ◽  
Static Experiment ◽  
Acoustic Emission Test ◽  
Assessing Method ◽  
Non Destructive

Acoustic emission (AE) test monitoring is an effective non-destructive technique. In the paper, a new damage assessing method which is damage acuteness index for AE signal of PZT patches based on fractal theory was proposed. The damage index was deduced by the character of signal analysed by fractal theory. It is deduced that both the curve length and the fractal dimension (FD) of signal are related with damage development. The AE test of Pseudo-static experiment of a concrete-filled GFRP tubes (CFFT) was performed for validation. The results show that the damage acuteness index can assess damage development process effectively. So the damage acuteness index is a promising method to apply in AE test monitoring.

Infrared Thermography and Acoustic Emission in 2D Plain Woven C/Sic Composites under Tensile-Tensile Fatigue Loading

2010 ◽  
Vol 118-120 ◽  
pp. 251-255 ◽  
Author(s):  
Bin Li ◽  
Xiao Yan Tong ◽  
Zi Yang Feng ◽  
Lei Jiang Yao
Keyword(s):  
Acoustic Emission ◽  
Applied Stress ◽  
Infrared Camera ◽  
Wide Band ◽  
Fatigue Loading ◽  
Thermal Dissipation ◽  
Tensile Fatigue ◽  
Ae Signal ◽  
Sic Composites ◽  
Non Destructive

Plain plate specimens of 2D plain woven C/SiC composites were performed on Instron8801. Infrared (IR) thermography was recorded using an infrared camera. Acoustic emission (AE) signal was detected by two AE wide band sensors attached on specimen. They were measured synchronously and real-timely. Thermal dissipation Q was deduced based on the first law of thermodynamics. When the applied stress was lower than fatigue endurance limit, Q rose in the early cyclic loading stage and then the rate of Q accumulation gradually approached a steady value as the proceeding cycles, conversely, Q rose quickly until led to failure of the composites. AE accumulated energy was discussed based on the AE data. Higher applied stress would cause more damage within the composites, and more AE signals were detected. Compared with damage calculated from modulus, Q and AE accumulated energy had fairly well agreement with the damage. It can be concluded that it is possible to employ these non-destructive evaluation methods as in-situ damage evolution indicators for 2D C/SiC composites.

Detection and Monitoring of Fatigue Cracks in Metallic Structures Using Acoustic Emission: Routes to Quantification of Probability of Detection

2014 ◽  
Vol 891-892 ◽  
pp. 1268-1274 ◽  
Author(s):  
Daniel Gagar ◽  
Peter Foote ◽  
Phil E. Irving
Keyword(s):  
Acoustic Emission ◽  
Fatigue Crack ◽  
Fatigue Cracks ◽  
Failure Process ◽  
Probability Of Detection ◽  
Metallic Structures ◽  
Final Failure ◽  
Ae Signal ◽  
Non Destructive ◽  
Ae Signals

The performance and reliability of Structural Health Monitoring (SHM) techniques remain largely unquantified. This is in contrast to the probability of detection (POD) and sensitivity of manual non destructive inspection methods which are well characterised. In this study factors influencing the rates of emission of Acoustic Emission (AE) signals from propagating fatigue cracks were investigated. Fatigue crack growth experiments were performed in 2014 T6 aluminium sheet to observe the effects of changes in crack length, loading spectrum and sample geometry on rates of emission and the probability of detecting and locating the fatigue crack. Significant variation was found in the rates of AE signal generation during crack progression from initiation to final failure. AE signals at any point in the failure process were found to result from different failure mechanisms operating at particular stages in the failure process.

scholarly journals Analysis of the results of acoustic emission diagnostics of a structure during helicopter fatigue tests

Aviation ◽  
2018 ◽  
Vol 21 (2) ◽  
pp. 64-69 ◽  
Author(s):  
Aleksandrs URBAHS ◽  
Kristine CARJOVA ◽  
Jurijs FESCUKS
Keyword(s):  
Acoustic Emission ◽  
Measurement Data ◽  
Fatigue Tests ◽  
Parameter Analysis ◽  
Inspection Method ◽  
Characteristic Structure ◽  
Ae Signal ◽  
Diagnostic Measurement ◽  
Non Destructive ◽  
Ae Signals

The study is devoted to a perspective diagnostic method, which makes it possible to deal with diagnostic tasks – the acoustic non-destructive inspection method based on acoustic emission (AE) signal parameter analysis. The practical use of this method is related to the interpretation of diagnostic measurement data. The parameters of acoustic emission (AE) signals were measured during bench tests of the tail boom structure and fin, as well as the joint areas of the fin, tail boom, and fuselage of the helicopter (joint area No.1 and No.19, frames of the tail boom and fuselage respectively).The analysis of fatigue damage kinetics was carried out in several stages for groups of bolts and for characteristic structure loading intervals. Bolt fracture was predicted at least 26 to 44 flight hours before the actual collapse. Using the AE parameters, the micro crack origin intervals identified when the bolt bearing capacity after the occurrence of the damage reached 96%.

AE Fault Diagnosis of Rolling Bearing Based on Base

2011 ◽  
Vol 143-144 ◽  
pp. 664-668
Author(s):  
D.L. Yang ◽  
X.J. Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Dimensionality Reduction ◽  
Rolling Bearing ◽  
Acoustic Emission Sensors ◽  
Ae Signal ◽  
Acoustic Emission Test ◽  
Fault Source ◽  
Original Feature

In the acoustic emission fault diagnosis, the acoustic emission sensors was installed on the bearing pedestal where near from the fault source so that can collected stronger fault AE signal, however ,sometimes, it is inconvenience for AE sensor installation. This paper proposed that install the AE sensor on the base for collect the fault AE signal, but the signal was weak, so carried on EMD first, and selected the former 8 IMF to construct the original feature, than carried on KPCA for dimensionality reduction and get the optimized feature. In this paper, taking bearing acoustic emission test for example, by compared the base fault feature with the bearing pedestal fault feature, verified that the method that the AE sensor install on the base is feasible.

Damage Progression and Residual Strength Evaluation in Sandwich Panels Loaded at the Center

2011 ◽  
Vol 465 ◽  
pp. 527-530 ◽  
Author(s):  
Amir Refahi Oskouei ◽  
Milad Hajikhani ◽  
Mehdi Ahmadi Najaf Abadi ◽  
Amir Sharifi ◽  
Mohammad Heidari
Keyword(s):  
Acoustic Emission ◽  
Residual Strength ◽  
Mechanical Energy ◽  
Sandwich Panels ◽  
Static Test ◽  
Loading Level ◽  
Out Of Plane ◽  
Ae Signal ◽  
Diamond Saw ◽  
Non Destructive

This paper addresses damage evaluation of loaded sandwich panels by using acoustic emission (AE) as a non-destructive method. The specimens were loaded monotonically out-of-plane in control of displacement and the tests were stopped at three different damage levels. Each loading level activates different failure mechanism that can influence on residual strength of material. After each quasi-static test, the damaged plate was cut by a diamond saw to obtain tensile specimens. After cutting, compression test carried out by using acoustic emission to monitor the process. Depend on loading level the damage value was variant as it caused different residual strength that was related to acoustic emission signals activities. There is a relation between AE signal energy and mechanical energy that can follow to evaluate the residual strength of panels in different loading level in sandwich panels. Results show that the using AE method having mechanical results can be effective in residual strength and progressive damage evolution.

Detection of the Propagation of Defects in Pressurized Pipes Through the Acoustic Emission Technique Using Artificial Neural Networks

2006 ◽  
Author(s):  
Sergio Damasceno Soares ◽  
Romeu Ricardo da Silva
Keyword(s):  
Neural Networks ◽  
Acoustic Emission ◽  
Artificial Neural Networks ◽  
Non Destructive Testing ◽  
Destructive Testing ◽  
Emission Testing ◽  
Acoustic Emission Testing ◽  
Artificial Neural ◽  
Acoustic Emission Test ◽  
Non Destructive

The acoustic emission test has distinguished relevance in non-destructive testing and, therefore, existing research abound at present aiming at the improvement of the reliability of their results. In this work, the methodologies and the results obtained in a study performed are presented to implement pattern classifiers by using artificial neural networks, aiming at the propagation of existing defects in pressurized pipes by means of Acoustic Emission testing (AE). Parameters that are characteristic of AE signals were used as input data for the classifiers. Several tests were performed and the classification performances were in the range of 92% for most of the instances analyzed. Studies of parameter relevance were also performed and showed that only a few of the parameters are actually important for the separation of classes of signals corresponding to No Propagation (NP) of defects and Propagation (P) of defects. The results obtained are pioneering in this type of research and encouraged the present publication.

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