Fault Diagnosis of Planetary Gearboxes Based on Self-attentive Mechanism Capsule Network
A fault diagnosis method based on self-attentive mechanism capsule network is proposed to solve the problems of limited fault data and low diagnosis accuracy for planetary gearboxes in practical engineering.The acquired planetary gearbox vibration signal is directly used as the input to extract primary features through the first wide convolutional layer and filter the high-frequency noise in the input.The self-attentive mechanism is introduced to focus on the key features of the signal.The proposed features are input into the capsule layer to further extract features and achieve fault classification.The proposed method is verified by the data of planetary gearbox experimental platform.The results show that the proposed method can still achieve good diagnostic accuracy with limited samples.
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