LAPSE:2019.1286
Published Article
LAPSE:2019.1286
A Fault Feature Extraction Method for the Fluid Pressure Signal of Hydraulic Pumps Based on Autogram
December 9, 2019
Center spring wear faults in hydraulic pumps can cause fluid pressure fluctuations at the outlet, and the fault feature information on fluctuations is often contaminated by different types of fluid flow interferences. Aiming to resolve the above problems, a fluid pressure signal method for hydraulic pumps based on Autogram was applied to extract the fault feature information. Firstly, maximal overlap discrete wavelet packet transform (MODWPT) was adopted to decompose the contaminated fault pressure signal of center spring wear. Secondly, based on the squared envelope of each node, three kinds of kurtosis of unbiased autocorrelation (AC) were computed in order to describe the fault feature information comprehensively. These are known as standard Autogram, upper Autogram and lower Autogram. Then a node corresponding to the biggest kurtosis value was selected as a data source for further spectrum analysis. Lastly, the data source was processed by threshold values, and then the fault could be diagnosed based on the fluid pressure signal.
Record ID
Keywords
Autogram, feature extraction, fluid pressure, hydraulic pump, kurtosis
Subject
Suggested Citation
Zheng Z, Li X, Zhu Y. A Fault Feature Extraction Method for the Fluid Pressure Signal of Hydraulic Pumps Based on Autogram. (2019). LAPSE:2019.1286
Author Affiliations
Zheng Z: College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China
Li X: College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China
Zhu Y: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China [ORCID]
Li X: College of Mechanical Engineering, North China University of Science and Technology, Tangshan 063210, China
Zhu Y: National Research Center of Pumps, Jiangsu University, Zhenjiang 212013, China [ORCID]
Journal Name
Processes
Volume
7
Issue
10
Article Number
E695
Year
2019
Publication Date
2019-10-03
Published Version
ISSN
2227-9717
Version Comments
Original Submission
Other Meta
PII: pr7100695, Publication Type: Journal Article
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LAPSE:2019.1286
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External Link
doi:10.3390/pr7100695
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Version History
[v1] (Original Submission)
Dec 9, 2019
Verified by curator on
Dec 9, 2019
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v1
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https://psecommunity.org/LAPSE:2019.1286
Original Submitter
Calvin Tsay
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