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| Intelligent Detection Method of Variable Load Gearbox Fault Signal |
| SHI Pei-ming1,ZHAO Na1,SU Guan-hua1,SONG Tao2,HAN Dong2 |
1. School of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. Qinhuangdao Audiovisual Machinery Research Institute, Qinhuangdao, Hebei 066000, China |
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Abstract Aiming at the problem of gear fault feature extraction and classification under variable load excitation, an intelligent detection method of gearbox based on empirical mode decomposition (EMD) and deep belief network (DBN) is presented. Therefore, the intrinsic mode function (IMF) of the meshing frequency and the frequency doubling is selected to reconstruct the signal, and obtain the spectrum of the reconstructed signal, which is the input of the deep belief network. In the deep belief network, the pre-training and feature learning of input spectrum are carried out, and the classification model of gear fault recognition based on variable load excitation is established. Finally, fault diagnosis is carried out by using the constructed deep belief network. The experimental results show that the proposed method can effectively identify gear failure types under variable load excitation.
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Received: 16 January 2017
Published: 06 November 2018
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Corresponding Authors:
Peiming Shi
E-mail: spm@ysu.edu.cn
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［1］Lou X S, Loparo K A. Bearing fault diagnosis based on wavelettransform and fuzzy inference［J］. Mechanical Systems and Signal Processing, 2004, 18: 1077-1095.
［2］Yang D L, Liu Y L, Li S B, et al. Gear fault diagnosis based on support vector machine optimized by artificial bee colony algorithm［J］. Mechanism and Machine Theory,2015, 90: 219-229.
［3］Ma R,Chen Y S,Cao Q J. Research ondynamics and fault mechanism of spur gear pair with spallingdefect［J］. Journal of Sound and Vibration,2012, 331: 2097-2109.
［4］孟宗, 谷明伟, 胡猛. 基于改进奇异值分解和经验模式分解的滚动轴承早期微弱故障特征提取［J］. 计量学报, 2016, 37(4): 406-410.
［5］时培明, 王敬, 温江涛. 基于包络线算法改进EMD的旋转机械故障诊断方法研究［J］. 计量学报, 2016, 37(1): 62-66.
［6］刘彬, 蒋金水, 于伟凯. EMD 相关度去噪及其在轧机信号处理中的应用［J］. 计量学报, 2009, 30(1): 73-77.
［7］Fu Y, Zhang Y, Qiao H, et al. Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks ［J］. Procedia Cirp, 2015, 31: 29-34.
［8］Tran V T, Althobiani F, Ball A. An approach to fault diagnosis of reciprocating compressor valves using Teager-Kaiser energy operator and deep belief networks［J］.Expert Syst Appl, 2014,41(9): 4113-4122.
［9］Vapnik V N . The Nature of Statistical Learning Theory Second Edition［M］. Berlin : Springer-Ver-lag, 2000. |
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2018, Vol. 39 
