A novel cross-validated nondestructive evaluation framework for damage detection using acoustic emission

2021 ◽  
Author(s):  
Prashanth Abraham Vanniamparambil
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
Nondestructive Evaluation ◽  
Evaluation Framework

APPLICATION OF THE EARLY DAMAGE DIAGNOSTICS TECHNIQUE TO EXAMINATION OF THE AVIATION PANEL

2019 ◽  
Vol 85 (6) ◽  
pp. 53-63 ◽  
Author(s):  
I. E. Vasil’ev ◽  
Yu. G. Matvienko ◽  
A. V. Pankov ◽  
A. G. Kalinin
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
High Speed ◽  
Early Stage ◽  
Video Data ◽  
High Speed Video ◽  
Damage Diagnostics ◽  
Subsurface Defect ◽  
Sensitive Coating ◽  
Early Damage

The results of using early damage diagnostics technique (developed in the Mechanical Engineering Research Institute of the Russian Academy of Sciences (IMASH RAN) for detecting the latent damage of an aviation panel made of composite material upon bench tensile tests are presented. We have assessed the capabilities of the developed technique and software regarding damage detection at the early stage of panel loading in conditions of elastic strain of the material using brittle strain-sensitive coating and simultaneous crack detection in the coating with a high-speed video camera “Video-print” and acoustic emission system “A-Line 32D.” When revealing a subsurface defect (a notch of the middle stringer) of the aviation panel, the general concept of damage detection at the early stage of loading in conditions of elastic behavior of the material was also tested in the course of the experiment, as well as the software specially developed for cluster analysis and classification of detected location pulses along with the equipment and software for simultaneous recording of video data flows and arrays of acoustic emission (AE) data. Synchronous recording of video images and AE pulses ensured precise control of the cracking process in the brittle strain-sensitive coating (tensocoating)at all stages of the experiment, whereas the use of structural-phenomenological approach kept track of the main trends in damage accumulation at different structural levels and identify the sources of their origin when classifying recorded AE data arrays. The combined use of oxide tensocoatings and high-speed video recording synchronized with the AE control system, provide the possibility of definite determination of the subsurface defect, reveal the maximum principal strains in the area of crack formation, quantify them and identify the main sources of AE signals upon monitoring the state of the aviation panel under loading P = 90 kN, which is about 12% of the critical load.

Damage detection of offshore platforms using acoustic emission analysis

2018 ◽  
Vol 89 (11) ◽  
pp. 115005 ◽  
Author(s):  
Guijie Liu ◽  
Shirui Wang ◽  
Yingchun Xie ◽  
Xiaojie Tian ◽  
Dingxin Leng ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Damage Detection ◽  
Offshore Platforms ◽  
Emission Analysis ◽  
Acoustic Emission Analysis

scholarly journals Damage Analysis and Prediction in Glass Fiber Reinforced Polyester Composite Using Acoustic Emission and Machine Learning

2021 ◽  
Author(s):  
S. Gholizadeh
Keyword(s):  
Machine Learning ◽  
Acoustic Emission ◽  
Damage Detection ◽  
Glass Fiber ◽  
Fatigue Cracking ◽  
Great Success ◽  
Fiber Reinforced ◽  
Structural Problems ◽  
Glass Fiber Reinforced ◽  
Polyester Composite

One of the most pervasive types of structural problems in aircraft industries is fatigue cracking that can potentially occur without anticipation with catastrophic failures and unexpected downtime. Acoustic emission (AE) is a passive structural health monitoring (SHM) technique, since it offers real time damage detection based on stress waves generated by cracking in the structure. Machine learning techniques have presented great success over the past few years with a large number of applications. This study assesses the progression of damage occurring on glass fiber reinforced polyester composite specimens using two approaches of machine learning, namely, Supervised and Unsupervised learning. A methodology for damage detection and characterization of composite is presented. The result shows that machine learning can predict the failure. All predictive models and their performance as well as AE parameters had a direct relationship with the applied stress values, suggesting that these correlation coefficients are reliable means of predicting fatigue life in a composite material.

scholarly journals Damage Source Identification of Reinforced Concrete Structure Using Acoustic Emission Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-5
Author(s):  
Alireza Panjsetooni ◽  
Norazura Muhamad Bunnori ◽  
Amir Hossein Vakili
Keyword(s):  
Acoustic Emission ◽  
Reinforced Concrete ◽  
Nondestructive Evaluation ◽  
Analysis Method ◽  
Reinforced Concrete Structure ◽  
Acoustic Emission Technique ◽  
Rc Structures ◽  
Rc Frames ◽  
Damage Monitoring ◽  
Investigation Process

Acoustic emission (AE) technique is one of the nondestructive evaluation (NDE) techniques that have been considered as the prime candidate for structural health and damage monitoring in loaded structures. This technique was employed for investigation process of damage in reinforced concrete (RC) frame specimens. A number of reinforced concrete RC frames were tested under loading cycle and were simultaneously monitored using AE. The AE test data were analyzed using the AE source location analysis method. The results showed that AE technique is suitable to identify the sources location of damage in RC structures.

scholarly journals Efficient generation of receiver operating characteristics for the evaluation of damage detection in practical structural health monitoring applications

2017 ◽  
Vol 473 (2199) ◽  
pp. 20160736 ◽  
Author(s):  
Chang Liu ◽  
Jacob Dobson ◽  
Peter Cawley
Keyword(s):  
Experimental Data ◽  
Damage Detection ◽  
Evaluation Framework ◽  
Operating Characteristics ◽  
Damage Growth ◽  
Flat Bottom ◽  
Temperature Cycles ◽  
Bottom Hole ◽  
Wide Range ◽  
Receiver Operating

Permanently installed guided wave monitoring systems are attractive for monitoring large structures. By frequently interrogating the test structure over a long period of time, such systems have the potential to detect defects much earlier than with conventional one-off inspection, and reduce the time and labour cost involved. However, for the systems to be accepted under real operational conditions, their damage detection performance needs to be evaluated in these practical settings. The receiver operating characteristic (ROC) is an established performance metric for one-off inspections, but the generation of the ROC requires many test structures with realistic damage growth at different locations and different environmental conditions, and this is often impractical. In this paper, we propose an evaluation framework using experimental data collected over multiple environmental cycles on an undamaged structure with synthetic damage signatures added by superposition. Recent advances in computation power enable examples covering a wide range of practical scenarios to be generated, and for multiple cases of each scenario to be tested so that the statistics of the performance can be evaluated. The proposed methodology has been demonstrated using data collected from a laboratory pipe specimen over many temperature cycles, superposed with damage signatures predicted for a flat-bottom hole growing at different rates at various locations. Three damage detection schemes, conventional baseline subtraction, singular value decomposition (SVD) and independent component analysis (ICA), have been evaluated. It has been shown that in all cases, the component methods perform significantly better than the residual method, with ICA generally the better of the two. The results have been validated using experimental data monitoring a pipe in which a flat-bottom hole was drilled and enlarged over successive temperature cycles. The methodology can be used to evaluate the performance of an installed monitoring system and to show whether it is capable of detecting particular damage growth at any given location. It will enable monitoring results to be evaluated rigorously and will be valuable in the development of safety cases.

Advanced Sensing of Materials Processing

1988 ◽  
Vol 117 ◽  
Author(s):  
Haydn N. G. Wadley ◽  
Arnold H. Kahn ◽  
Ward Johnson
Keyword(s):  
Acoustic Emission ◽  
Process Control ◽  
Nondestructive Evaluation ◽  
Eddy Current ◽  
Control Strategies ◽  
Measurement Methods ◽  
Materials Processing ◽  
Intelligent Processing ◽  
New Process

AbstractTo implement new process control strategies including Intelligent Processing of Materials, advanced sensors are required to nonintrusively evaluate process and microstructure variables. Researchers increasingly are looking to innovative extensions of traditional nondestructive evaluation technologies, such as ultrasonics, acoustic emission, and eddy current methodologies for this. Here, the nature and characteristics of emerging sensors based upon these new measurement methods are described and examples of their application discussed.

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