  Fault diagnosis plays an essential role in reducing the maintenance costs of
rotating machinery manufacturing systems. In many real applications of fault
detection and diagnosis, data tend to be imbalanced, meaning that the number of
samples for some fault classes is much less than the normal data samples. At
the same time, in an industrial condition, accelerometers encounter high levels
of disruptive signals and the collected samples turn out to be heavily noisy.
As a consequence, many traditional Fault Detection and Diagnosis (FDD)
frameworks get poor classification performances when dealing with real-world
circumstances. Three main solutions have been proposed in the literature to
cope with this problem: (1) the implementation of generative algorithms to
increase the amount of under-represented input samples, (2) the employment of a
classifier being powerful to learn from imbalanced and noisy data, (3) the
development of an efficient data pre-processing including feature extraction
and data augmentation. This paper proposes a hybrid framework which uses the
three aforementioned components to achieve an effective signal-based FDD system
for imbalanced conditions. Specifically, it first extracts the fault features,
using Fourier and wavelet transforms to make full use of the signals. Then, it
employs Wasserstein Generative Adversarial Networks (WGAN) to generate
synthetic samples to populate the rare fault class and enhance the training
set. Moreover, to achieve a higher performance a novel combination of
Convolutional Long Short-term Memory (CLSTM) and Weighted Extreme Learning
Machine (WELM) is proposed. To verify the effectiveness of the developed
framework, different datasets settings on different imbalance severities and
noise degrees were used. The comparative results demonstrate that in different
scenarios GAN-CLSTM-ELM outperforms the other state-of-the-art FDD frameworks.
