Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy

Chuanqi Lu; Shaoping Wang; Mileta Tomovic

doi:10.1109/ICIEA.2015.7334162

Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy

Chuanqi Lu
, Shaoping Wang
, Mileta Tomovic

School of Automation Science and Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

Based on Empirical Mode Decomposition (EMD) and feature energy entropy, a method for the fault severity recognition of piston pumps is proposed in this paper. The discharge pressure signals of piston pumps are decomposed into a series of Intrinsic Mode Function (IMF) components by using EMD. Then, some useful IMF components are selected by calculating correlation coefficient between the signal reconstructed by the selected IMFs and the original signal. The characteristic vector is constructed by computing the normalized energy of every selected IMF, and the feature energy entropy can be obtained. The experimental results indicate that the proposed method can recognize the fault severity of pumps effectively.

Original language	English
Title of host publication	Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	489-494
Number of pages	6
ISBN (Electronic)	9781467373173
DOIs	https://doi.org/10.1109/ICIEA.2015.7334162
State	Published - 20 Nov 2015
Event	10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 - Auckland, New Zealand Duration: 15 Jun 2015 → 17 Jun 2015

Publication series

Name	Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015

Conference

Conference	10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015
Country/Territory	New Zealand
City	Auckland
Period	15/06/15 → 17/06/15

Keywords

EMD
Hydraulic piston pump
fault severity recognition
feature energy entropy

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10.1109/ICIEA.2015.7334162

Cite this

Lu, C., Wang, S., & Tomovic, M. (2015). Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 (pp. 489-494). Article 7334162 (Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA.2015.7334162

Lu, Chuanqi ; Wang, Shaoping ; Tomovic, Mileta. / Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy. Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 489-494 (Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015).

@inproceedings{c35da6639f3e47c2989292c075207a84,

title = "Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy",

abstract = "Based on Empirical Mode Decomposition (EMD) and feature energy entropy, a method for the fault severity recognition of piston pumps is proposed in this paper. The discharge pressure signals of piston pumps are decomposed into a series of Intrinsic Mode Function (IMF) components by using EMD. Then, some useful IMF components are selected by calculating correlation coefficient between the signal reconstructed by the selected IMFs and the original signal. The characteristic vector is constructed by computing the normalized energy of every selected IMF, and the feature energy entropy can be obtained. The experimental results indicate that the proposed method can recognize the fault severity of pumps effectively.",

keywords = "EMD, Hydraulic piston pump, fault severity recognition, feature energy entropy",

author = "Chuanqi Lu and Shaoping Wang and Mileta Tomovic",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015 ; Conference date: 15-06-2015 Through 17-06-2015",

year = "2015",

month = nov,

day = "20",

doi = "10.1109/ICIEA.2015.7334162",

language = "英语",

series = "Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "489--494",

booktitle = "Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015",

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Lu, C, Wang, S & Tomovic, M 2015, Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy. in Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015., 7334162, Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, Institute of Electrical and Electronics Engineers Inc., pp. 489-494, 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015, Auckland, New Zealand, 15/06/15. https://doi.org/10.1109/ICIEA.2015.7334162

Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy. / Lu, Chuanqi; Wang, Shaoping; Tomovic, Mileta.
Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 489-494 7334162 (Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy

AU - Lu, Chuanqi

AU - Wang, Shaoping

AU - Tomovic, Mileta

PY - 2015/11/20

Y1 - 2015/11/20

N2 - Based on Empirical Mode Decomposition (EMD) and feature energy entropy, a method for the fault severity recognition of piston pumps is proposed in this paper. The discharge pressure signals of piston pumps are decomposed into a series of Intrinsic Mode Function (IMF) components by using EMD. Then, some useful IMF components are selected by calculating correlation coefficient between the signal reconstructed by the selected IMFs and the original signal. The characteristic vector is constructed by computing the normalized energy of every selected IMF, and the feature energy entropy can be obtained. The experimental results indicate that the proposed method can recognize the fault severity of pumps effectively.

AB - Based on Empirical Mode Decomposition (EMD) and feature energy entropy, a method for the fault severity recognition of piston pumps is proposed in this paper. The discharge pressure signals of piston pumps are decomposed into a series of Intrinsic Mode Function (IMF) components by using EMD. Then, some useful IMF components are selected by calculating correlation coefficient between the signal reconstructed by the selected IMFs and the original signal. The characteristic vector is constructed by computing the normalized energy of every selected IMF, and the feature energy entropy can be obtained. The experimental results indicate that the proposed method can recognize the fault severity of pumps effectively.

KW - EMD

KW - Hydraulic piston pump

KW - fault severity recognition

KW - feature energy entropy

UR - https://www.scopus.com/pages/publications/84960929426

U2 - 10.1109/ICIEA.2015.7334162

DO - 10.1109/ICIEA.2015.7334162

M3 - 会议稿件

AN - SCOPUS:84960929426

T3 - Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015

SP - 489

EP - 494

BT - Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015

Y2 - 15 June 2015 through 17 June 2015

ER -

Lu C, Wang S, Tomovic M. Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy. In Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 489-494. 7334162. (Proceedings of the 2015 10th IEEE Conference on Industrial Electronics and Applications, ICIEA 2015). doi: 10.1109/ICIEA.2015.7334162

Fault severity recognition of hydraulic piston pumps based on EMD and feature energy entropy

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