Clustering Group-Sparse Mode Decomposition and Its Application in Rolling Bearing Fault Diagnosis-Reference-Cited by-同舟云学术

Clustering Group-Sparse Mode Decomposition and Its Application in Rolling Bearing Fault Diagnosis

Published:2022 Issue: Volume:71 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Pang Bin¹^ORCID,Wang Bocheng²^ORCID,Hu Yuzhi¹^ORCID,Cheng Tianshi¹^ORCID,Xu Zhenli³^ORCID

Affiliation:

1. National and Local Joint Engineering Research Center of Metrology Instrument and System and the College of Quality and Technical Supervision, Hebei University, Baoding, China

2. College of Cyberspace Security and Computer, Hebei University, Baoding, China

3. Department of Mechanical Engineering, North China Electric Power University, Baoding, China

Funder

Natural Science Foundation of Hebei Province, China

Hebei University High-Level Talents Research Start Project

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09885236.pdf?arnumber=9885236

Reference39 articles.

1. The FERgram: A rolling bearing compound fault diagnosis based on maximal overlap discrete wavelet packet transform and fault energy ratio

2. Bootstrap aggregative mean shift clustering for big data anti-pattern detection analytics in 5G/6G communication networks

3. An Enhanced Hybrid Clustering Approach for Privacy Preservation (ECPS) in Big Data using Apache Spark Framework

4. A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation

5. Mean Shift Clustering-Based Analysis of Nonstationary Vibration Signals for Machinery Diagnostics

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