Work

Acoustic emission hit detection with autoregressive filtering and sequential probability ratio test

Year: 2026

Type: conference-paper

Abstract: In this publication, we examine state of the art hit detection algorithms and evaluate their performance using real measurement data. We demonstrate that the combination of Autoregressive (AR) filtering and the Sequential Probability Ratio Test (SPRT) forms a robust and effective approach for hit detection tasks in Acoustic Emission (AE) measurements. Using the AR+SPRT approach, acoustic hits can be detected earlier, even in noisy environments. We review existing hit detection algorithms and evaluate their performance using real measurement data. We also introduce a modified version of the classical AR+SPRT framework, which employs a tri contaminated normal distribution for the sampling plan hypothesis. The parameters of this distribution are tailored specifically to the requirements of AE hit detection.

Source: IEEE International Conference on Intelligent Engineering Systems (INES)

DOI: 10.1109/ines69513.2026.11661144

Authors: Tibor Dobján, Gábor Pór, Eva Osztenyine Krauczi, Szilveszter Pletl

Institutions: John von Neumann University, University of Dunaújváros, University of Szeged

Language: English

Topic: Ultrasonics and Acoustic Wave Propagation

Subfield: Mechanics of Materials

Field: Engineering

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W7204155064

FWCI:

Cites: 14

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Related to: 10