An Approach of Filtering to Select IMFs of EEMD in Signal Processing for Acoustic Emission [AE] Sensors

2018 ◽  
pp. 100-111 ◽  
Author(s):  
Nur Syakirah Mohd Jaafar ◽  
Izzatdin Abdul Aziz ◽  
Jafreezal Jaafar ◽  
Ahmad Kamil Mahmood
Keyword(s):  
Signal Processing ◽  
Acoustic Emission

scholarly journals Potential of Empirical Mode Decomposition for Hilbert Demodulation of Acoustic Emission Signals in Gearbox Diagnostics

2021 ◽  
Author(s):  
Félix Leaman ◽  
Cristián Molina Vicuña ◽  
Elisabeth Clausen
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Fault Diagnosis ◽  
Bandpass Filter ◽  
Processing Technique ◽  
Signal Processing Technique ◽  
Mode Decomposition ◽  
Gear Fault ◽  
Envelope Spectrum ◽  
Ae Signals

Abstract Background The acoustic emission (AE) analysis has been used increasingly for gearbox diagnostics. Since AE signals are of non-linear, non-stationary and broadband nature, traditional signal processing techniques such as envelope spectrum must be carefully applied to avoid a wrong fault diagnosis. One signal processing technique that has been used to enhance the demodulation process for vibration signals is the empirical mode decomposition (EMD). Until now, the combination of both techniques has not yet been used to improve the fault diagnostics in gearboxes using AE signals. Purpose In this research we explore the use of the EMD to improve the demodulation process of AE signals using the Hilbert transform and enhance the representation of a gear fault in the envelope spectrum. Methods AE signals were measured on a planetary gearbox (PG) with a ring gear fault. A comparative signal analysis was conducted for the envelope spectra of the original AE signals and the obtained intrinsic mode functions (IMFs) considering three types of filters: highpass filter in the whole AE range, bandpass filter based on IMF spectra analysis and bandpass filter based on the fast kurtogram. Results It is demonstrated how the results of the envelope spectrum analysis can be improved by the selection of the relevant frequency band of the IMF most affected by the fault. Moreover, not considering a complementary signal processing technique such as the EMD prior the calculation of the envelope of AE signals can lead to a wrong fault diagnosis in gearboxes. Conclusion The EMD has the potential to reveal frequency bands in AE signals that are most affected by a fault and improve the demodulation process of these signals. Further research shall focus on overcome issues of the EMD technique to enhance its application to AE signals.

Application of signal processing to acoustic emission signals emitted by steel specimens during tensile tests

Ultrasonics ◽  
1982 ◽  
Vol 20 (1) ◽  
pp. 18-24 ◽  
Author(s):  
I. Lanchon-Magnin ◽  
P. Fleischmann ◽  
D. Rouby ◽  
R. Goutte
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Tensile Tests

Optical-Fiber Sensors for the Detection of Acoustic Emission

MRS Bulletin ◽  
2002 ◽  
Vol 27 (5) ◽  
pp. 396-399 ◽  
Author(s):  
William B. Spillman ◽  
Richard O. Claus
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Optical Fiber ◽  
Material Properties ◽  
High Speed ◽  
Optical Fiber Sensors ◽  
Piezoelectric Transducers ◽  
Fiber Sensors ◽  
Critical Materials ◽  
Signal Processing Methods

AbstractAcoustic emission (AE) is used as a means to anticipate the mechanical failure of critical materials and structures by detecting the release of energy caused by material rearrangement at the microlevel. Optical-fiber sensors have potential advantages over conventional tuned piezoelectric transducers and signal-processing methods for the detection of such types of ultrasonic acoustic wave events. A number of fiber Bragg grating techniques are presented, which in particular offer the potential to provide the high-speed signal processing and ability to multiplex numbers of AE sensors necessary to detect, quantify, and locate AE sources and thereby determine material properties and damage.

Wavelet Signal Processing of Acoustic Emission Data

2001 ◽  
Vol 204-205 ◽  
pp. 351-358 ◽  
Author(s):  
W.J. Staszewski ◽  
Karen M. Holford
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Emission Data

scholarly journals Wavelet application in acoustic emission signal detection of wire related events in pipeline

2021 ◽  
Author(s):  
Ran Wu
Keyword(s):  
Signal Processing ◽  
Acoustic Emission ◽  
Wavelet Transform ◽  
Signal Detection ◽  
Time Scale ◽  
Inspection Time ◽  
Time Frequency ◽  
Pipeline Inspection ◽  
Ae Signal ◽  
Thesis Work

This thesis establishes an automatic classification program for the signal detection work in pipeline inspection. Time-scale analysis provides the basic methodology of this thesis work. The wavelet transform is implemented in the program for filtering out the majority of noise and detect needed signals. As a popular nondestructive test, acoustic emission (AE) testing has been widely used in many physical and engineering fields such as leak detection and pipeline inspection. Among those applied AE tests, a common problem is to extract the physical features of the ideal events, so as to detect similar signals. In acoustic signal processing, those features can be represented as joint time frequency distribution. However, classical signal processing methods only give global information on either time or frequency domain, while local information is lots. Although the short-time Fourier transform (STFT) is developed to analyze time and frequency details simultaneously, it can only achieve limited precision. Other time-frequency methods are also applied in AE signal processing, but they all have the problem of resolution and time consuming. Wavelet transform is a time-scale technique with adaptable precision, which makes better feature extraction and detail detection. This thesis is an application of wavelet transform in AE signal detection where various noise exists. The wavelet transform with Morelet wavelet as the mother wavelet provides the basis of the program for auto classification in this thesis work. Finally the program is tested with two industrial projects to verify the workability of wavelet transforms and the reliability of the developed auto classifiers.

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