An adaptive extraction method for rail crack acoustic emission signal under strong wheel-rail rolling noise of high-speed railway

2021 ◽  
Vol 154 ◽  
pp. 107546
Author(s):  
Qiushi Hao ◽  
Yi Shen ◽  
Yan Wang ◽  
Jian Liu
Keyword(s):  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Extraction Method ◽  
High Speed ◽  
High Speed Railway ◽  
Emission Signal ◽  
Rolling Noise

An extraction method of welding crack acoustic emission signal using harmonic analysis

Measurement ◽  
2017 ◽  
Vol 103 ◽  
pp. 311-320 ◽  
Author(s):  
Kuanfang He ◽  
Xiangnan Liu ◽  
Qing Yang ◽  
Yong Chen
Keyword(s):  
Acoustic Emission ◽  
Harmonic Analysis ◽  
Acoustic Emission Signal ◽  
Extraction Method ◽  
Emission Signal

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.

Signatures of Acoustic Emission Signals Generated During High Speed Cutting

2001 ◽  
Author(s):  
Andre Sitz ◽  
Udo Schwarz ◽  
J. Kurths ◽  
Doris Maus ◽  
Michael Wiese ◽  
...  
Keyword(s):  
Acoustic Emission ◽  
Data Analysis ◽  
Differential Equations ◽  
Frequency Domain ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Cutting Process ◽  
High Speed Cutting ◽  
Emission Signal ◽  
Linear And Nonlinear

Abstract Acoustic emission signals generated during high speed cutting of steel are investigated. The data are represented in time-folded form. Several methods from linear and nonlinear data analysis based on time- and frequency-domain are applied to the data and reveal signatures of the observed acoustic emission signal. These investigations are first steps for modeling the cutting process by means of differential equations.

Development of Acoustic Emission High-Speed Data Acquisition System

2012 ◽  
Vol 433-440 ◽  
pp. 5666-5671 ◽  
Author(s):  
Ting You ◽  
Pei Jiang Li ◽  
Guan Jun Tong ◽  
Jian Wei Shen
Keyword(s):  
Acoustic Emission ◽  
Real Time ◽  
Acoustic Emission Signal ◽  
High Speed ◽  
Transmission Rate ◽  
Acquisition System ◽  
High Frequency Signal ◽  
Emission Signal ◽  
High Speed Data Acquisition ◽  
High Speed Data

Acoustic emission is generally high-frequency signal with the characteristics of broadband and burstiness, so it is very difficult to acquire acoustic emission signal in real time as well as rapidly, In this paper, an USB based acquisition system for acoustic emission signal is designed, which adopts CPLD as controller and uses two high-speed A/D converters to achieve synchronous acquisition of two-channel acoustic emission signals, The system first puts collected data into a FIFO and then transfers data to host through USB using CY7C68013, The sampling frequency of the system is up to 10MHz and the transmission rate of USB is 40M/S, Hard body impact test and lead-breaking test indicate that the system can achieve real-time acquisition of acoustic emission signal well,

Research of the cold resistance of metals by indentation with registration of an acoustic emission signal

2020 ◽  
pp. 61-64
Author(s):  
Yu.G. Kabaldin ◽  
A.A. Khlybov ◽  
M.S. Anosov ◽  
D.A. Shatagin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Acoustic Emission ◽  
Acoustic Emission Signal ◽  
Low Temperatures ◽  
Cold Resistance ◽  
Emission Signal ◽  
Impact Bending ◽  
Artificial Neural

The study of metals in impact bending and indentation is considered. A bench is developed for assessing the character of failure on the example of 45 steel at low temperatures using the classification of acoustic emission signal pulses and a trained artificial neural network. The results of fractographic studies of samples on impact bending correlate well with the results of pulse recognition in the acoustic emission signal. Keywords acoustic emission, classification, artificial neural network, low temperature, character of failure, hardness. [email protected]

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