Abstract:
The rotor fault diagnosis plays an important role in rotating machinery
system by early detection and avoiding dangerous situations of rotating ma chinery system. Accurate fault diagnosis of rotor machine ensures reliability
and security of rotor mechanical systems. There are many existing methods
for the rotor fault diagnosis, in these methods have a problem of data defi ciency. To solve the problem of data deficiency and improving efficiency of
model, introduced a method is called GAN-AE based rotor fault diagnosis.
The data deficiency problem of existing methods are solved by using a Gen erative Adversarial Network (GAN) model, GAN model generating a series
of new synthetic samples from the original data samples and they are similar
to the original data sample and it aim to expand the original raw sample .
The generated synthetic samples are utilized as the training samples to train
the classifier and to identify the unknown faults. The GAN generated signal
combined to the original dataset and then it given to the Auto Encoder (AE)
model. These complete data is given to the auto encoder model and it extracts
the signal features and it provides better accuracy than the Auto Encoder
model. In this work, GAN model is combining with AE model for the rotor
fault diagnosis. GAN-AE method solve insufficient fault samples problem in
more complex mechanical system with agreeable fault classification accuracy.
The GAN-AE method can offer better capability of extracting features, and
the accuracy of fault diagnosis of rotating machines. MAFAULDA database
is used as the base experimental dataset. Experimental results and analysis
show that this GAN-AE based rotor fault diagnosis has better performance
