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Fault diagnosis for motor bearing based on vibro-acoustic signal fusion and WR-VMD
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Chengyi JIN1, Jianpeng CHEN2, Wei CHENG1, Zhengguo XU2, 3
Thermal Power Generation | 2024, 53(11) : 101 - 111
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Thermal Power Generation | 2024, 53(11): 101-111
Thermal energy science research
Fault diagnosis for motor bearing based on vibro-acoustic signal fusion and WR-VMD
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Chengyi JIN1, Jianpeng CHEN2, Wei CHENG1, Zhengguo XU2, 3
Affiliations
  • 1.China Nuclear Power Engineering Co., Ltd., Shenzhen 518120, China
  • 2.Huzhou Institute of Zhejiang University, Huzhou 313000, China
  • 3.College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
Published: 2024-11-25 doi: 10.19666/j.rlfd.202407173
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In cooling systems of thermal and nuclear power generating unit, the bearing fault signal of the motor is weak and nonlinear, which is easily masked by running signals and invalid signals, and the use of a single vibration monitoring may not be sufficient to collect complete defect information. To address this problem, vibration and sound signals are combined to monitor bearing fault signals, and the collected sound and vibration signal features are fused. To process the sound and vibration signals of motor bearings, a WR-VMD algorithm that integrates wavelet ridge (WR) and varational mode decomposition (VMD) is proposed. The WR is used to analyze the components of the original signal, and then the acquired information is used to determine the parameters of the VMD, which makes up for the shortcomings of the original VMD method that requires the parameters to be set empirically in advance. The simulated signal results show that, compared with the same type of methods, the features extracted by the WR-VMD method are the most obvious and have the least interference information. Finally, the acoustic and vibration signal fusion technique and the WR-VMD algorithm are applied to the measured motor bearing fault data, and the results show that, compared with other feature extraction algorithms of the same type, the WR-VMD extracts the most obvious features and has the highest accuracy in fault diagnosis. The acoustic and vibration signal fusion has at least a 7% increase in accuracy compared with a single vibration or acoustic signal in fault diagnosis.

bearing faults  /  vibro-acoustic signal fusion  /  VMD  /  wavelet ridge line  /  feature extraction
Chengyi JIN, Jianpeng CHEN, Wei CHENG, Zhengguo XU. Fault diagnosis for motor bearing based on vibro-acoustic signal fusion and WR-VMD[J]. Thermal Power Generation, 2024 , 53 (11) : 101 -111 . DOI: 10.19666/j.rlfd.202407173
  • Science and Technology Project of China General Nuclear Power Group(K-A2023.560)
Year 2024 volume 53 Issue 11
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Article Info
doi: 10.19666/j.rlfd.202407173
  • Receive Date:2024-07-24
  • Online Date:2026-03-05
  • Published:2024-11-25
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History
  • Received:2024-07-24
Funding
Science and Technology Project of China General Nuclear Power Group(K-A2023.560)
Affiliations
    1.China Nuclear Power Engineering Co., Ltd., Shenzhen 518120, China
    2.Huzhou Institute of Zhejiang University, Huzhou 313000, China
    3.College of Control Science and Engineering, Zhejiang University, Hangzhou 310027, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202407173
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
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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