This paper proposes a novel data preprocessing method that converts numeric data into
representative graphs (polygons) expressing all of the relationships between data variables …

Specialty and fine chemicals are often manufactured in multipurpose batch production
plants. Compared to continuous production, these plants offer increased flexibility at the cost …

Digital twin (DT) is drawing significant attention both from the academia, industry and
government. However, people from different fields have different understandings and …

The digitalization of processes is one of the currently dominating topics and missions in both
industrial production and scientific research. In biotechnology, these efforts have enormous …

Abstract Analysis of industrial data imposes several challenges. These data are acquired
from heterogeneous sources such as sensors, cameras, IoT, etc, and are stored in different …

Current market developments lead in several domains to new challenges in terms of
production flexibility, a shortening of time-to-market and a more individualized production of” …

Multivariate time-series classification problems are found in many industrial settings; for
example, fault detection in a manufacturing process by monitoring sensors signals. It is …

Monitoring of flow regimes in aerated stirred tanks is important to ensure energy efficiency
and product quality. The use of deep learning models for the recognition of flow regimes …

Machine learning (ML) has become a popular tool in the industrial sector as it helps to
improve operations, increase efficiency, and reduce costs. However, deploying and …

Classification with multivariate signal data is an important machine learning task in artificial
intelligence applications in the process industry. Examples of such applications range from …