High Cycle Fatigue Damage Monitoring Based on Bhattacharyya Coefficient of Acoustic Emission

This study investigates the application of acoustic emission (AE) signals for monitoring fatigue damage during high-cycle fatigue (HCF) tests and proposes a fatigue damage monitoring strategy based on the Bhattacharyya coefficient (BC). A comprehensive description of the AE signal processing workflow is provided, encompassing the acquisition of signal waveform data, the calculation of probability distributions, and the utilization of the BC to quantify the similarity between reference and true distributions. Comparative analysis with traditional infrared thermography demonstrates that BC exhibits superior sensitivity in HCF tests, effectively capturing the progressive stages of fatigue damage. Additionally, the study evaluates the influence of varying bin widths on BC calculations and identifies an optimal bin width (BW). The optimal bin width not only balances the monitoring sensitivity and stability but also reduces the influence of external noise.

Authors

Institutions

Publication Details

Journal
Russian Journal of Nondestructive Testing
Published
2025-03-01
DOI
https://doi.org/10.1134/s1061830925600303
Citations
1
Primary Topic
Machine Fault Diagnosis Techniques
Type
article
Field-Weighted Citation Impact
1.21
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

High Cycle Fatigue Damage Monitoring Based on Bhattacharyya Coefficient of Acoustic Emission

Qingzhao Zhou, Bangchun Wen
1 citations
Russian Journal of Nondestructive Testing
Machine Fault Diagnosis Techniques
1.21
article

High Cycle Fatigue Damage Monitoring Based on Bhattacharyya Coefficient of Acoustic Emission

Qingzhao Zhou, Bangchun Wen
article en
1 citations

Abstract

This study investigates the application of acoustic emission (AE) signals for monitoring fatigue damage during high-cycle fatigue (HCF) tests and proposes a fatigue damage monitoring strategy based on the Bhattacharyya coefficient (BC). A comprehensive description of the AE signal processing workflow is provided, encompassing the acquisition of signal waveform data, the calculation of probability distributions, and the utilization of the BC to quantify the similarity between reference and true distributions. Comparative analysis with traditional infrared thermography demonstrates that BC exhibits superior sensitivity in HCF tests, effectively capturing the progressive stages of fatigue damage. Additionally, the study evaluates the influence of varying bin widths on BC calculations and identifies an optimal bin width (BW). The optimal bin width not only balances the monitoring sensitivity and stability but also reduces the influence of external noise.

Russian Journal of Nondestructive TestingVol. 61(3)
Northeastern University (CN)
Climate action
Openalex Percentile: Top 19%
Machine Fault Diagnosis Techniques
1.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.