Improving Production Efficiency with a Digital Twin Based on Anomaly Detection

Abstract

Industry 4.0, cyber-physical systems, and digital twins are generating ever more data. This opens new opportunities for companies, as they can monitor development and production processes, improve their products, and offer additional services. However, companies are often overwhelmed by Big Data, as they cannot handle its volume, velocity, and variety. Additionally, they mostly do not follow a strategy in the collection and usage of data, which leads to unexploited business potentials. This paper presents the implementation of a Digital Twin module in an industrial case study, applying a concept for guiding companies on their way from data to value. A standardized use case template and a procedure model support the companies in (1) formulating a value proposition, (2) analyzing the current process, and (3) conceptualizing a target process. The presented use case entails an anomaly detection algorithm based on Gaussian processes to detect defective products in real-time for the extrusion process of aluminum profiles. The module was initially tested in a relevant environment; however, full implementation is still missing. Therefore, technology readiness level 6 (TRL6) was reached. Furthermore, the effect of the target process on production efficiency is evaluated, leading to significant cost reduction, energy savings, and quality improvements.

Holistic Approach to the Ecological Evaluation of Digitalization Systems in the Production Environment

“…There exist various approaches and methods that focus on resource efficiency and digitalization. A significant part of them handles the advantages that digitalization brings with it to increase resource efficiency, relates to the use of specific technologies and describes the possible saving potential [13][14][15][16][17]. A holistic assessment about a comparison of the saving possibilities with the resources to be used only takes place in exceptional cases [18].…”

Review of synergy between machine learning and first principles models for asset integrity management

“…• Improved anomaly detection: Machine learning models can be used to detect anomalies in asset data, such as vibration or temperature readings, that may indicate the potential for failure. By combining machine learning with digital twin technology, it is possible to simulate the behavior of an asset under different conditions, identifying potential anomalies before they become a problem (Trauer, Pfingstl, Finsterer, & Zimmermann, 2021). • Improved design and optimization: First principles models can be used in combination with digital twin technology to optimize the design of an asset and its components.…”

Resilient Design of Product Service Systems with Automated Guided Vehicles

“…For this kind of mechanism, analytical models of sub-systems of the PSS are usually required. Complex, multi-domain simulation models which allow bidirectional data exchange are referred to as digital twins [54]. The application of digital twins can support many features which increase the resilience of PSSs and can enable service of PSSs with AGVs, such as scheduling and synchronization [55].…”