Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network

Joint Authors

Chen, Kai
Zhou, Xin-Cong
Fang, Jun-Qiang
Zheng, Peng-fei
Wang, Jun

Source

International Journal of Rotating Machinery

Issue

Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-10, 10 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2017-06-11

Country of Publication

Egypt

No. of Pages

10

Main Subjects

Mechanical Engineering

Abstract EN

A gear transmission system is a complex nonstationary and nonlinear time-varying coupling system.

When faults occur on gear system, it is difficult to extract the fault feature.

In this paper, a novel fault diagnosis method based on ensemble empirical mode decomposition (EEMD) and Deep Briefs Network (DBN) is proposed to treat the vibration signals measured from gearbox.

The original data is decomposed into a set of intrinsic mode functions (IMFs) using EEMD, and then main IMFs were chosen for reconstructed signal to suppress abnormal interference from noise.

The reconstructed signals were regarded as input of DBN to identify gearbox working states and fault types.

To verify the effectiveness of the EEMD-DBN in detecting the faults, a series of gear fault simulate experiments at different states were carried out.

Results showed that the proposed method which coupled EEMD and DBN can improve the accuracy of gear fault identification and it is capable of applying to fault diagnosis in practical application.

American Psychological Association (APA)

Chen, Kai& Zhou, Xin-Cong& Fang, Jun-Qiang& Zheng, Peng-fei& Wang, Jun. 2017. Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network. International Journal of Rotating Machinery،Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1169660

Modern Language Association (MLA)

Chen, Kai…[et al.]. Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network. International Journal of Rotating Machinery No. 2017 (2017), pp.1-10.
https://search.emarefa.net/detail/BIM-1169660

American Medical Association (AMA)

Chen, Kai& Zhou, Xin-Cong& Fang, Jun-Qiang& Zheng, Peng-fei& Wang, Jun. Fault Feature Extraction and Diagnosis of Gearbox Based on EEMD and Deep Briefs Network. International Journal of Rotating Machinery. 2017. Vol. 2017, no. 2017, pp.1-10.
https://search.emarefa.net/detail/BIM-1169660

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1169660