Generation of synthetic data with low-dimensional features for condition monitoring utilizing Generative Adversarial Networks
Fabian Wagner, Timo König, Moritz Benninger, Markus Kley, Liebschner Marcus
Abstract
Condition monitoring of machine elements for end-of-line quality control is an essential part of product assurance in many production areas. Therefore, the monitored machine elements are often separated in different condition classes and are classified via conventional signal processing and machine learning methods. Especially for machine learning algorithms, collection of a sufficient amount of data from each class is particularly relevant so that balanced training datasets, regarding the condition classes, are available. In reality, however, in most cases significantly more data is captured from good system states. This results in unbalanced data sets, which can be counteracted with synthetically generated data. Generative Adversarial Networks (GAN) are a suitable approach to generate synthetic measurement data. In the scope of this paper, a use case is considered, in which bearings are monitored in an end-of-line control via acoustic signals. The generation of such data requires a high computational effort. To reduce this effort, a suitable signal pre-processing method is presented, which allows the reduction of the generated feature dimension. The synthetic data based on low-dimensional features is subsequently evaluated regarding its suitability for the condition classification. It is shown that with synthetically generated data with low-dimensional features a similar classification accuracy can be achieved as with real data, making the synthetic data suitable for data augmentation in the use case.