Features of Noise Filtering During Acoustic Emission Testing
V. Barat1, V. Bardakov2

1Barat Vera*, department of Diagnostic Information Technologies, National Research University (MPEI), Moscow, Russian Federation. Email:
2Bardakov Vladimir, Department of Scientific Research, Interunis-IT, Moscow, Russian Federation. 

Manuscript received on October 16, 2019. | Revised Manuscript received on 25 October, 2019. | Manuscript published on November 10, 2019. | PP: 3977-3980 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5067119119/2019©BEIESP | DOI: 10.35940/ijitee.A5067.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This article discusses the problems of the acoustic emission method of non-destructive testing. An approach to filtering noise arising from monitoring of acoustic emission is considered. The filtering of acoustic noise is one of the key problems of the acoustic emission method, since the low noise immunity of the acoustic emission method prevents the expansion of its industrial application. The complexity of the filtering is explained by the fact that the waveform and spectrum of acoustic emission pulses change depending on the distance between the defect, which is the source of acoustic emission waves, and the sensor. In turn, the interference, as a rule, is non-stationary in nature and is determined by the type of technological process of the tested composition. This article discusses various types of noise processes, both stationary and non-stationary. The signal and noise parameters are compared, based on which recommendations are given for constructing algorithms for detecting acoustic emission pulses against a background of noise.
Keywords: Acoustic Emission, Noise Filtering, Friction Noise filtering
Scope of the Article: Soft computing Signal and Speech Processing