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Semi-supervised gear fault diagnosis using raw vibration signal based on deep learning
تشخیص خطای دنده نیمه نظارت شده با استفاده از سیگنال لرزش خام بر اساس یادگیری عمیق-2019 In aerospace industry, gears are the most common parts of a mechanical transmission
system. Gear pitting faults could cause the transmission system to crash and give rise to safety disaster.
It is always a challenging problem to diagnose the gear pitting condition directly through the
raw signal of vibration. In this paper, a novel method named augmented deep sparse autoencoder
(ADSAE) is proposed. The method can be used to diagnose the gear pitting fault with relatively few
raw vibration signal data. This method is mainly based on the theory of pitting fault diagnosis and
creatively combines with both data augmentation ideology and the deep sparse autoencoder algorithm
for the fault diagnosis of gear wear. The effectiveness of the proposed method is validated by
experiments of six types of gear pitting conditions. The results show that the ADSAE method can
effectively increase the network generalization ability and robustness with very high accuracy. This
method can effectively diagnose different gear pitting conditions and show the obvious trend
according to the severity of gear wear faults. The results obtained by the ADSAE method proposed
in this paper are compared with those obtained by other common deep learning methods. This
paper provides an important insight into the field of gear fault diagnosis based on deep learning
and has a potential practical application value. KEYWORDS : Deep learning | Gear pitting diagnosis | Gear teeth | Raw vibration signal | Semi-supervised learning | Sparse autoencoder |
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