عنوان انگلیسی مقاله:
Deep learning fault diagnosis method based on global optimization GAN for unbalanced data
ترجمه فارسی عنوان مقاله:
روش تشخیص گسل عمیق مبتنی بر بهینه سازی جهانی GAN برای داده های نامتوازن
Sciencedirect - Elsevier - Knowledge-Based Systems, Corrected proof: doi:10:1016/j:knosys:2019:07:008
Funa Zhou a,b, Shuai Yang b,∗, Hamido Fujita c, Danmin Chen d, Chenglin Wene
Deep learning can be applied to the field of fault diagnosis for its powerful feature representation
capabilities. When a certain class fault samples available are very limited, it is inevitably to be
unbalanced. The fault feature extracted from unbalanced data via deep learning is inaccurate, which
can lead to high misclassification rate. To solve this problem, new generator and discriminator of
Generative Adversarial Network (GAN) are designed in this paper to generate more discriminant
fault samples using a scheme of global optimization. The generator is designed to generate those
fault feature extracted from a few fault samples via Auto Encoder (AE) instead of fault data sample.
The training of the generator is guided by fault feature and fault diagnosis error instead of the
statistical coincidence of traditional GAN. The discriminator is designed to filter the unqualified
generated samples in the sense that qualified samples are helpful for more accurate fault diagnosis.
The experimental results of rolling bearings verify the effectiveness of the proposed algorithm.
Keywords: Fault diagnosis | Unbalance data | Global optimization | GAN | Deep learning