Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

Authors

Institutions

Publication Details

Journal
Journal of Physics Conference Series
Published
2024-10-01
DOI
https://doi.org/10.1088/1742-6596/2854/1/012075
Primary Topic
Cavitation Phenomena in Pumps
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Chao Feng, Beibei Chen, Hualun Zhu, Mengzi Yang
Journal of Physics Conference Series
Cavitation Phenomena in Pumps
article

Research on Rapid Detection Method of Cavitation State of Water jet Propulsion Pump Based on Multi-source and Multi-feature of Acoustic Emission Signal and Bayesian Network

Chao Feng, Beibei Chen, Hualun Zhu, Mengzi Yang
article en

Abstract

Abstract Cavitation prevents the conversion of internal dynamic and pressure energy in a water jet propulsion pump and causes erosion of flow passage components. Acoustic emission is a non-destructive test method for instability signals. Based on the incipient mechanism and development process of cavitation, three acoustic emission signal feature parameters (ring counting rate, energy release rate, Hurst index) are proposed to define the cavitation state utilizing the waveform pattern and signal self-similarity. The multi-source and multi-feature cavitation identification model of acoustic emission signals is established through cavitation simulation, multi-monitoring point acoustic emission signal testing, and feature parameter extraction, and a large number of probabilistic trainings are carried out using Bayesian network, resulting in a fast cavitation state identification method based on acoustic emission signals. The research results have important practical engineering application significance for the prediction of cavitation resistance, real-time dynamic monitoring of cavitation phenomenon and prevention of cavitation damage.

Journal of Physics Conference SeriesVol. 2854(1)
Shanghai Architectural Design & Research Institute (CN)
Openalex Percentile: Top 80%
Cavitation Phenomena in Pumps
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.