Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences-Reference-Cited by-同舟云学术

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Generalized Demodulation Transform for Bearing Fault Diagnosis Under Nonstationary Conditions and Gear Noise Interferences

Published:2019-02-01 Issue:1 Volume:32 Page:
ISSN:1000-9345
Container-title:Chinese Journal of Mechanical Engineering
language:en
Short-container-title:Chin. J. Mech. Eng.

Author:

Zhao Dezun,Li Jianyong,Cheng Weidong,He Zhiyang

Funder

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Mechanical Engineering

Link

http://link.springer.com/content/pdf/10.1186/s10033-019-0322-1.pdf

Reference30 articles.

1. K J Shi, S L Liu, J Chao, et al. Rolling bearing feature frequency extraction using extreme average envelope decomposition. Chinese Journal of Mechanical Engineering, 2016, 29(5): 1029-1036.

2. H Y Cui, Y Y Qiao, Y M Yin, et al. An investigation of rolling bearing early diagnosis based on high-frequency characteristics and self-adaptive wavelet de-noising. Neurocomputing, 2016, 216: 649-656.

3. Y F Li, M J Zuo, K Feng. Detection of bearing faults using a novel adaptive morphological update lifting wavelet. Chinese Journal of Mechanical Engineering, 2017, 30(6): 1305-1313.

4. F C Li, M Guang, Y Lin. Wavelet transform-based higher-order statistics for fault diagnosis in rolling element bearings. Journal of Vibration and Control, 2008, 14(11): 1691-1709.

5. B Murugantham, M A Sanjith, B Krishnakumar, et al. Roller element bearing fault diagnosis using singular spectrum analysis. Mechanical Systems and Signal Processing, 2013, 35(1): 150-166.

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