Realistic Ultrasonic Environment Simulation Using Conditional Generative Adversarial Networks. Recently, realistic data augmentation using neural networks especially generative neural networks (GAN) has achieved outstanding results. The communities main research focus is visual image processing. However, automotive cars and robots are equipped with a large suite of sensors to achieve a high redundancy. In addition to others, ultrasonic sensors are often used due to their lowcosts and reliable near field distance measuring capabilities. Hence, Pattern recognition needs to be applied to ultrasonic signals as well. Machine Learning requires extensive data sets and those measurements are timeconsuming, expensive and not flexible to hardware and environmental changes. On the other hand, there exists no method to simulate those signals deterministically. We present a novel approach for synthetic ultrasonic signal simulation using conditional GANs (cGANs). For the best of our knowledge, we present the first realistic data augmentation for automotive ultrasonics. The performance of cGANs allows us to bring the realistic environment simulation to a new level. By using setup and environmental parameters as condition, the proposed approach is flexible to external influences. Due to the low complexity and time effort for data generation, we outperform other simulation algorithms, such as finite element method. We verify the outstanding accuracy and realism of our method by applying a detailed statistical analysis and comparing the generated data to an extensive amount of measured signals.

CWGAN: Conditional Wasserstein Generative Adversarial Nets for Fault Data Generation With the rapid development of modern industry and artificial intelligence technology, fault diagnosis technology has become more automated and intelligent. The deep learning based fault diagnosis model has achieved significant advantages over the traditional fault diagnosis method. However, a problem has arisen when deep learning models are applied to actual industrial scenarios. In the actual process of industrial production, there are not enough fault data for deep learning, hence the accuracy will decrease because of overfitting. In this paper, aiming at the problem, the fault data generation based on deep learning is deeply studied.In this paper, the data source is the experimental data from the Fault Data Center of Case Western Reserve University.Aiming at the problem of small amount of fault data, a method of generating fault timefrequency spectrum based on improved conditional generative adversarial networks is proposed. CWGAN (Conditional Wasserstein Generative Adversarial Nets) learns the feature of timefrequency spectrum of rolling bearing fault, and generates timefrequency spectrum of corresponding fault categories according to the input categories. Experiments show that the diversity and fidelity of data generated by CWGAN is better than that of the original generative adversarial networks. The VGGNet model is used to train the fault data enhanced by CWGAN. It is found that the data generated by CWGAN can effectively supplement the small amount of fault data, improve the training effect of the model and avoid overfitting.