System Development & Application
|
App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 6
1758-1761,1766

Research on bearing fault diagnosis based on wavelet packet-auto regressive model spectrum and deep learning

He Siyan1
Liu Ya2
Tian Xincheng2
1. Dept. of Intelligent Manufacturing Engineering, Shandong College of Electronic Technology, Jinan 250200, China
2. School of Control Science & Engineering, Shandong University, Jinan 250061, China
DOI: 10.19734/j.issn.1001-3695.2018.04.0261

Abstract

As the bearing fault signal is non-stationary and non-linear in nature, this paper used the wavelet packet decomposition(WPD) and auto-regressive(AR) spectrum estimating method to extract features. In order to improve the accuracy of diagnosis, this paper proposed the deep believe network to train diagnostic models. Firstly, it used the WPD and AR spectrum estimating method to calculate the energy of different signal bands to realize the feature extraction of vibration signals. Then, it used the extracted eigenvalues as input vectors of the deep belief network to conduct model training. Finally, it used the trained model to carry out bearing fault diagnosis. In order to verify the effectiveness of the proposed method, used the rotating bearing data set provided by the Case Western Reserve University in the United States, the algorithm was compared with three fault diagnosis methods. Experimental results show that the proposed method has better diagnostic performance.

Foundation Support

山东省重点研发计划资助项目(2016ZDJS02B03)
山东省重大科技创新工程项目(2017CXGC0601)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.04.0261
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 6
Section: System Development & Application
Pages: 1758-1761,1766
Serial Number: 1001-3695(2019)06-033-1758-04

Publish History

[2019-06-05] Printed Article

Cite This Article

贺思艳, 刘亚, 田新诚. 基于小波包-AR谱和深度学习的轴承故障诊断研究 [J]. 计算机应用研究, 2019, 36 (6): 1758-1761,1766. (He Siyan, Liu Ya, Tian Xincheng. Research on bearing fault diagnosis based on wavelet packet-auto regressive model spectrum and deep learning [J]. Application Research of Computers, 2019, 36 (6): 1758-1761,1766. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)