Fault diagnosis of rotating machinery using Gaussian process and EEMD-treelet

Author:

Wu Edmond Qi1ORCID,Wang Jin2,Peng Xian-Yong3ORCID,Zhang Peidong1,Law Rob4,Chen Xi5,Lin Jin-xing6ORCID

Affiliation:

1. Department of Automation; Shanghai Jiao Tong University; Shanghai China

2. Beijing Institute of Spacecraft Environment Engineering; Beijing China

3. Tengyi Data Technologies Shanghai Co., Ltd; Shanghai China

4. School of Hotel and Tourism Management; Hong Kong Polytechnic University; Hung Hom Hong Kong

5. Shanghai Engineering Research Center of Civil Aircraft Health Monitoring, COMAC; Shanghai China

6. College of Automation; Nanjing University of Posts and Telecommunications; Nanjing China

Funder

National Natural Science Foundation of China

Military Commission's Advanced Research Fund

Shanghai Sailing Program

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Signal Processing,Control and Systems Engineering

Reference48 articles.

1. Towards automatic detection of local bearing defects in rotating machines;Ericsson;Mech Syst Signal Process,2005

2. A comparison study of improved Hilbert-Huang transform and wavelet transform: application to fault diagnosis for rolling bearing;Peng;Mech Syst Signal Process,2005

3. State of the art in monitoring rotating machinery-part 2;Randall;Sound Vib,2004

4. State of the art in monitoring rotating machinery-part 1;Randall;Sound Vib,2004

5. A comparative study on classification of features by SVM and PSVM extracted using Morlet wavelet for fault diagnosis of spur bevel gear box;Saravanan;Expert Syst Appl,2008

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