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Building Robust Classifiers with Generative Adversarial Networks for Detecting Cavitation in Hydraulic Turbines

In this paper a convolutional neural network (CNN) with high ability for generalization is build. The task of the network is to predict the occurrence of cavitation in hydraulic turbines independent from sensor position and turbine type. The CNN is directly trained on acoustic spectrograms, obtained form acoustic emission sensors operating in the ultrasonic range. Since gathering training data is expensive, in terms of limiting accessibility to hydraulic turbines, generative adversarial networks (GAN) are utilized in order to create fake training data. GANs consist basically of two parts. The first part, the generator, has the task to create fake input data, which ideally is not distinguishable form real data. The second part, the discriminator, has the task to distinguish between real and fake data. In this work an Auxiliary Classifier-GAN (AC-GAN) is build. The discriminator of an AC-GAN has the additional task to predict the class label. After successful training it is possible to obtain a robust classifier out of the discriminator. The performance of the classifier is evaluated on separate