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Fault mode recognition of planetary gears based on CNN and transfer learning

Jiangen Tang  Yingyong Zou  Jun Yu  Yongde Zhang  
【摘要】:Due to the advantages of high running accuracy,small space requirement and large transmission efficiency,planetary gearboxes have been extensive used in industry.The vibration signal obtained by the vibration acceleration sensor is unstable at high temperature and severe environment.To solve this problem,a fault diagnosis method of planetary gears using CNN and transfer learning is suggested in this paperto transfer fault recognition knowledge of planetary gears.First,source domain datas are used as input to the network in order to obtain training parameters.The training parameters are used as the starting parameters of the target domain.Domain adaptation is used to narrow the domain difference of feature migration.After model training,this method uses softmax classifier to identify states of planetary gear among target domain data.The effectiveness of this method is verified by the results of fault diagnosis experiments.

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