A Novel Deep Feature Learning Method Based on the Fused-Stacked AEs for Planetary Gear Fault Diagnosis
作者： Xihui Chen , Aimin Ji , Gang Cheng , None
DOI: 10.3390/EN12234522
关键词:
摘要: Planetary gear is the key component of transmission system electromechanical equipment for energy industry, and it easy to damage, which affects reliability operation efficiency industry. Therefore, great significance extract useful fault features diagnose faults based on raw vibration signals. In this paper, a novel deep feature learning method fused-stacked autoencoders (AEs) planetary diagnosis was proposed. First, improve data ability robustness extraction process AE model, sparse autoencoder (SAE) contractive (CAE) were studied, respectively. Then, quantum ant colony algorithm (QACA) used optimize specific location parameters SAEs CAEs in architecture, multiple stacked alternately form gave architecture better extraction. The experimental results show that proposed can address signals gear. Compared with other architectures shallow has performance, an effective diagnosis.
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