Enhanced classification of hydraulic testing of directional control valves with synthetic data generation

Abstract

Production environments bring inherent system challenges that are reflected in the high-dimensional production data. The data is often nonstationary, is not available in sufficient size and quality, and is class imbalanced due to the predominance of good parts. Data-driven manufacturing analytics requires data of sufficient quantity and quality. In order to predict quality characteristics, production data is collected across processes in the industrial use case at Bosch Rexroth AG for the purpose of inferring results in hydraulic final inspection using machine learning methods. Since high quality data generation is costly, synthetic data generation methodologies offer a promising alternative to improve prediction models and thus generate safer, more accurate predictions for manufacturing companies. Among the synthetic data generation methodologies used, variational autoencoders compared to generative adversarial networks and synthetic minority oversampling technique methods are best suited to synthesize the feature with highest feature importance from a small sample data set compared to the production data and improve the prediction for the target variable.

Exploring Innovative Approaches to Synthetic Tabular Data Generation

“…In an attempt to address the complexity of utilizing production data in the manufacturing industry for data-driven analytics, a potential solution involving the application of variational autoencoders, a form of synthetic data generation method, was proposed [12]. This research demonstrates the effective results of this approach in enhancing prediction models, which is particularly beneficial for manufacturing companies faced with limited and unbalanced data.…”