Open AccessLeakage Detection Based on CEEMDAN Analysis for Hydraulic Cylinder Using Acoustic Emission Technique
Author(s) -
Peng Zhang,
Xinyuan Chen,
Zhiwen Cheng
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2166/1/012064
Subject(s) - hilbert–huang transform , leakage (economics) , acoustic emission , acoustics , fault detection and isolation , amplitude , noise (video) , spectral leakage , vibration , computer science , physics , algorithm , artificial intelligence , optics , telecommunications , white noise , actuator , economics , image (mathematics) , macroeconomics , fast fourier transform
The early internal leakage fault characteristics in hydraulic cylinder are very weak and vulnerable to environmental noise, which makes the early internal leakage fault detection very difficult. In comparison with internal leakage detection by the pressure signal, internal leakage by Acoustic emission (AE) signal has higher sensitivity and accuracy. So this paper adapts the algorithms combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and auto regressive (AR) spectrum. Comparing with the mean value of first intrinsic mode function (IMF1) instantaneous amplitude based on EEMD, The experimental results verify the proposed algorithms based on CEEMDAN distinguish different internal leakage levels obviously and has better performance.