Structural damage monitoring for metallic panels based on acoustic emission and adaptive improvement variational mode decomposition–wavelet packet transform

2021 ◽  
pp. 147592172110089
Author(s):  
Yang Li ◽  
Feiyun Xu
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Background Noise ◽  
Wavelet Packet ◽  
External Signal ◽  
Emission Signal ◽  
Signal Features ◽  
Mode Decomposition ◽  
Damage Monitoring ◽  
Metallic Panels

The metallic panel acoustic emission signal with strong non-stationary properties is normally composed of multiple components (e.g. impulses, background noise, and other external signal), where impulses relevant to metallic panel are easily contaminated by background noise and other external signal, making it difficult to excavate the inherent acoustic emission signal features. To address this issue and achieve the damage monitoring of metallic panels based on acoustic emission technology, a new scheme based on adaptive improvement variational mode decomposition–wavelet packet transform is developed for extracting acoustic emission signal features of metallic panels. Specifically, three different dimensions of Q235B steel plates are utilized to collect acoustic emission signal during three-point bending experiments, to evaluate the effectiveness of the proposed approach and to investigate the influence of size effect on the acoustic emission signal characteristics. In addition, the transient process and centroid frequency distribution of each damage stage are investigated, and the internal structure variations in the bending damage process are detected by scanning electron microscopy inspection. Moreover, the generalization of the proposed damage monitoring method is evaluated for plate-like structures that have complex geometric features, such as welds. The results demonstrate the effectiveness of the proposed method for acoustic emission–based structural health monitoring of metallic plate-like structures.

The EMD Analysis AE Signals of Rock Failure under Uniaxial Compression

2014 ◽  
Vol 571-572 ◽  
pp. 845-852
Author(s):  
Tian Jun Zhang ◽  
Sheng Hong Yu ◽  
Jin Hu Ren ◽  
Wei Cui
Keyword(s):  
Acoustic Emission ◽  
Uniaxial Compression ◽  
Acoustic Emission Signal ◽  
Wavelet Packet ◽  
Testing Machine ◽  
Waveform Analysis ◽  
Mode Analysis ◽  
Emission Signal ◽  
Mode Decomposition ◽  
Emd Method

The wavelet packet basis is difficult to be extracted by wavelet analysis at present. To solve this problem, an experiment of Acoustic Emission under uniaxial compression is conducted by SAEU2S acoustic Emission system and Electro-hydraulic servo universal testing machine and the method of empirical mode analysis is adopted to explore the acoustic emission signal in this paper. Firstly with the method of empirical mode decomposition, the acoustic emission signal is decomposed into the forms of intrinsic mode function with several local time scale and residual components, and then these data is analyzed. After the noise-reducing IMF and residual components are refactored, the error between the final and the initial reconstruction signals is less than 10-6. The experiment indicates that the EMD method is effective in processing the local rock acoustic emission signals. The EMD method also provides an efficient way to predict deformation trend of rock damage through deformation of waveform analysis.

Evaluating the sensitivity of acoustic emission signal features to the variation of cutting parameters in milling aluminum alloys: Part A: frequency domain analysis

2020 ◽  
pp. 095440542094912
Author(s):  
Mohamad Javad Anahid ◽  
Hoda Heydarnia ◽  
Seyed Ali Niknam ◽  
Hedayeh Mehmanparast
Keyword(s):  
Acoustic Emission ◽  
Frequency Domain ◽  
Time Domain ◽  
Acoustic Emission Signal ◽  
Cutting Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Emission Signal ◽  
Signal Parameters ◽  
Signal Features

It is known that adequate knowledge of the sensitivity of acoustic emission signal parameters to various experimental parameters is indispensable. According to the review of the literature, a lack of knowledge was noticeable concerning the behavior of acoustic emission parameters under a broad range of machining parameters. This becomes more visible in milling operations that include sophisticated chip formation morphology and significant interaction effects and directional pressures and forces. To remedy the aforementioned lack of knowledge, the effect of the variation of cutting parameters on the time and frequency features of acoustic emission signals, extracted and computed from the milling operation, needs to be investigated in a wide aspect. The objective of this study is to investigate the effects of cutting parameters including the feed rate, cutting speed, depth of cut, material properties, as well as cutting tool coating/insert nose radius on computed acoustic emission signals featured in the frequency domain. Similar studies on time-domain signal features were already conducted. To conduct appropriate signal processing and feature extraction, a signal segmentation and processing approach is proposed based on dividing the recorded acoustic emission signals into three sections with specific signal durations associated with cutting tool movement within the work part. To define the sensitive acoustic emission parameters to the variation of cutting parameters, advanced signal processing and statistical approaches were used. Despite the time features of acoustic emission signals, frequency domain acoustic emission parameters seem to be insensitive to the variation of cutting parameters. Moreover, cutting factors governing the effectiveness of acoustic emission signal parameters are hinted. Among these, the cutting speed and feed rate seem to have the most noticeable effects on the variation of time–frequency domain acoustic emission signal information, respectively. The outcomes of this work, along with recently completed works in the time domain, can be integrated into advanced classification and artificial intelligence approaches for numerous applications, including real-time machining process monitoring.

The Research on Leak Detection Technology of Natural Gas Pipeline Based on EMD

2014 ◽  
Vol 494-495 ◽  
pp. 793-796 ◽  
Author(s):  
Chuan Jiang Li ◽  
Jia Pan Zhang ◽  
Zi Qiang Zhang ◽  
Ju Li Hu ◽  
Yi Li
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Leakage Detection ◽  
Intrinsic Mode Functions ◽  
Emission Signal ◽  
Natural Gas Pipeline ◽  
Detection Technology ◽  
Mode Decomposition ◽  
Leakage Characteristics ◽  
Signal Processing Methods

The acoustic emission signal of pipeline leakage is characterized by nonlinear and non-stationary. It is not feasible to extract the leakage feature signal in traditional signal processing methods. The leak locations can be detected by employing the improved empirical mode decomposition (EMD) to decompose the acoustic emission signal into several intrinsic mode functions (IMF), choosing IMFs containing leakage characteristics to be reconstructed, and doing correlation analysis. Experimental results show that the positioning accuracy of leakage detection is improved obviously.

Wavelet Packet analyses of Acoustic Emission Signal for Tool Wear in High Speed Milling

2013 ◽  
Vol 589-590 ◽  
pp. 600-605
Author(s):  
Shun Xing Wu ◽  
Peng Nan Li ◽  
Zhi Hui Yan ◽  
Li Na Zhang ◽  
Xin Yi Qiu ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Tool Wear ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Feature Vector ◽  
Wavelet Packet ◽  
High Speed Milling ◽  
Band Energy ◽  
Emission Signal ◽  
Frequency Band Energy

Tool wear condition monitoring technology is one of the main parts of advanced manufacturing technology and is a hot research direction in recent years. A method based on the characteristics of acoustic emission signal and the advantages of wavelet packets decomposition theory in the non-stationary signal feature extraction is proposed for tool wear state monitoring with monitor the change of acoustic emission signal feature vector. In this paper, through the method, firstly, acoustic emission signal were decomposed into 4 layers with wavelet packet analysis, secondly, the frequency band energy of the have been decomposed signal were extracted, thirdly, the frequency band energy that are sensitive to tool wear were selected as feature vector, and then the corresponding relation between feature vector and tool wear was established , finally, the state of the tool wear can be distinguished according to the change of feature vector. The results show that this method can be feasibility used to monitor tool wear state in high speed milling.

Research on Feature Extraction Experiment for Acoustic Emission Signal of Rotor Crack Fault

2010 ◽  
Vol 34-35 ◽  
pp. 1005-1009 ◽  
Author(s):  
Kuan Fang He ◽  
Xue Jun Li ◽  
X.C. Li
Keyword(s):  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Acoustic Emission Signal ◽  
Wavelet Packet ◽  
Fault Simulation ◽  
Early Period ◽  
Test Bed ◽  
Simulation Test ◽  
Emission Signal ◽  
Extraction Experiment

The acoustic emission extraction experiment of rotor crack fault has done on the rotor comprehensive fault simulation test-bed. Characteristics of acoustic emission signal of different crack rotors in various depth and conditions are analyzed. The noise-disturbing problems and the noise-eliminating methods of the acoustic emission signal were researched in the paper, and the comparison has been done with the vibration method of crack fault diagnosis by the experiment. The advantages of acoustic emission technique has been highlighted in the early period crack fault diagnosis. The wavelet packet technique was applied to obtain the characteristics of acoustic emission signal of the rotor crack propagation. the diagnosis results are shown to be quite clear, reliable and accurate.

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