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Instantaneous angular speed signal based rolling bearing fault diagnosis method by optimized AR model
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Yun-gui ZHU, Yu GUO, Xin CHEN, Xin-min YANG, Xiang ZOU
Journal of Vibration Engineering | 2024, 37(12) : 2141 - 2147
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Journal of Vibration Engineering | 2024, 37(12): 2141-2147
Instantaneous angular speed signal based rolling bearing fault diagnosis method by optimized AR model
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Yun-gui ZHU, Yu GUO, Xin CHEN, Xin-min YANG, Xiang ZOU
Affiliations
  • Faculty of Mechanical and Electrical Engineering，Kunming University of Science and Technology，Kunming 650500，China
Published: 2024-12-28 doi: 10.16385/j.cnki.issn.1004-4523.2024.12.016
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To address the issue of carrying rolling element bearing （REB） fault diagnosis where the conventional vibration sensor is difficult to install，an instantaneous angular speed （IAS） signal based REB fault diagnosis method by optimized AR model is proposed. The forward differential method is used to calculate and estimate the instantaneous angular speed signal. Then，the biased estimation autocorrelation analysis is used to determine the optimal order p by the maximum autocorrelation kurtosis. Periodic components in the IAS signal are removed by AR prediction，and the residual components containing rich bearing fault information are remained. The residual components are pre-whitened to equalize the importance of each band and to extract fault characteristics from the envelope. Simulation signal and outer ring data from a test rig validate the effectiveness of the proposed method. The experimental comparative analysis results show that the calculation efficiency is improved significantly when compared to the existing method of fast spectral steepness combined with order analysis based on vibration signal.

fault diagnosis  /  rolling bearing  /  AR model  /  biased estimation  /  IAS signal
Yun-gui ZHU, Yu GUO, Xin CHEN, Xin-min YANG, Xiang ZOU. Instantaneous angular speed signal based rolling bearing fault diagnosis method by optimized AR model[J]. Journal of Vibration Engineering, 2024 , 37 (12) : 2141 -2147 . DOI: 10.16385/j.cnki.issn.1004-4523.2024.12.016
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Year 2024 volume 37 Issue 12
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Article Info
doi: 10.16385/j.cnki.issn.1004-4523.2024.12.016
  • Receive Date：2022-10-28
  • Online Date：2026-02-12
  • Published：2024-12-28
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  • Received：2022-10-28
  • Revised：2022-12-24
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    Faculty of Mechanical and Electrical Engineering，Kunming University of Science and Technology，Kunming 650500，China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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