Henrik Meyer | Searcharxiv

arXiv2026

Analytics for the Optimization of the Soybean Oil Purification Process

Machine Learning, Artificial Intelligence, among others, are very promising methodologies and technologies that are emerging for implementing a broad spectrum of analytics within digitalized eco-systems. Analytics containing adequate analytical models are generating a burgeoning interest from Business Intelligence-, Information Technology (IT)- and Operational Technology (OT)-professionals, who are able to exploit the huge amount of internally and externally available data and information that lies behind digitalized components and systems and their associated processes. In this paper, the authors present the essential specifications of an analytical model developed and implemented applying the Knowldege Discovery in Databases (KDD) approach. The analytical model is the essential part of an analytics component, positioned as an digitalized asset within an Industry 4.0 compliant (RAMI 4.0) infrastructure, and used to optimize the industrial Soybean Oil Purification Process associated to the digitalized eco-system.

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arXiv2026

Time-Series Anomaly Detection for Mobile Robots in Automotive Active Safety Testing using an RNN-VAE

Mobile robots, like the ultra-flat overrunable (UFO) robot platform, used in automotive active safety tests, currently lack self-diagnostic capabilities necessary to detect present hardware defects. This circumstance can lead to more severe failures, causing expensive repairs and operational downtime. This work proposes, for the first time, a reconstructionbased time-series anomaly detection model for these mobile robots, considering defect classes such as unevenly worn full-rubber tires or damaged dampers. Unlike prior publications, the proposed approach leverages the vast quantities of unlabeled data generated during routine operation through a simple pre-training step. Furthermore, it optimizes the hyperparameters of the implemented gated recurrent unit-based variational autoencoder (GRU-VAE) and evaluates both a stateless, windowed training approach and one using truncated backpropagation through time (TBPTT). The model's generalization capabilities are demonstrated by successfully detecting six defect types, with four of them not present in the data used for hyperparameter optimization and threshold selection. This is validated using a test set collected from five system instances at various points over a period of several months, achieving an F1 score of 0.936, indicating strong practical viability.

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