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作者:

Fan, Ji (Fan, Ji.) | Qi, Yongsheng (Qi, Yongsheng.) | Gao, Xuejin (Gao, Xuejin.) (学者:高学金) | Li, Yongting (Li, Yongting.) | Wang, Lin (Wang, Lin.)

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摘要:

The rolling element bearings used in rotating machinery generally include multiple coexisting defects. However, individual defect-induced signals of bearings simultaneously arising from multiple defects are difficult to extract from measured vibration signals because the impulse-like fault signals are very weak, and the vibration signal is commonly affected by the transmission path and various sources of interference. This issue is addressed in this study by proposing a new compound fault feature extraction scheme. Vibration signals are first preprocessed using resonance-based signal sparse decomposition to obtain the low-resonance component of the signal, which contains the information related to the transient fault-induced impulse signals, and reduce the interference of discrete harmonic signal components and noise. The objective used for adaptively selecting the optimal resonance-based signal sparse decomposition parameters adopts the ratio of permutation entropy to the frequency domain kurtosis, as a new comprehensive index, and the optimization is conducted using the cuckoo search algorithm. Subsequently, we apply multipoint sparsity to the low-resonance component to automatically determine the possible number of impulse signals and their periods according to the peak multipoint sparsity values. This enables the targeted extraction and isolation of fault-induced impulse signal features by multipoint optimal minimum entropy deconvolution adjustment. Finally, the envelope spectrum of the filtered signal is used to identify the individual faults. The effectiveness of the proposed scheme is verified by its application to both simulated and experimental compound bearing fault vibration signals with strong interference, and its advantages are confirmed by comparisons of the results with those of an existing state-of-the-art method.

关键词:

resonance-based signal sparse decomposition Fault diagnosis multipoint sparsity multipoint optimal minimum entropy deconvolution adjustment

作者机构:

  • [ 1 ] [Fan, Ji]Inner Mongolia Univ Technol, Sch Elect Power, 49 Aimin St, Hohhot 010051, Inner Mongolia, Peoples R China
  • [ 2 ] [Qi, Yongsheng]Inner Mongolia Univ Technol, Sch Elect Power, 49 Aimin St, Hohhot 010051, Inner Mongolia, Peoples R China
  • [ 3 ] [Li, Yongting]Inner Mongolia Univ Technol, Sch Elect Power, 49 Aimin St, Hohhot 010051, Inner Mongolia, Peoples R China
  • [ 4 ] [Wang, Lin]Inner Mongolia Univ Technol, Sch Elect Power, 49 Aimin St, Hohhot 010051, Inner Mongolia, Peoples R China
  • [ 5 ] [Gao, Xuejin]Beijing Univ Technol, Fac Informat Technol, Beijing, Peoples R China

通讯作者信息:

  • [Qi, Yongsheng]Inner Mongolia Univ Technol, Sch Elect Power, 49 Aimin St, Hohhot 010051, Inner Mongolia, Peoples R China

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来源 :

JOURNAL OF VIBRATION AND CONTROL

ISSN: 1077-5463

年份: 2020

期: 11-12

卷: 27

页码: 1212-1230

2 . 8 0 0

JCR@2022

ESI学科: ENGINEERING;

ESI高被引阀值:115

被引次数:

WoS核心集被引频次: 19

SCOPUS被引频次: 20

ESI高被引论文在榜: 0 展开所有

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