Erstellung und Management von Informationsmodellen für bestehende Produktionssysteme

Schmied, Sebastian; Großmann, Daniel; Mueller, Ralph Klaus; Mathias, Selvine G.; Jumar, Ulrich

doi:10.1515/auto-2020-0021

Cited by 4 publications

(1 citation statement)

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“…As no reviewed papers provided quantifiable metrics to gauge the effectiveness of proposed solutions, our study underscores a clear gap in current research. IM is highly dependent on the understanding of the modeler and his experience and skills [74]. This statement also applies to the quality manager, who is responsible for the RCA.…”

Section: Discussionmentioning

confidence: 99%

Unlocking the Potential of Information Modeling for Root Cause Analysis in a Production Environment: A Comprehensive State-of-the-Art Review Using the Kitchenham Methodology

Koval,

Knollmeyer,

Mathias

et al. 2024

IEEE Access

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Data from production environments is now available in unprecedented volumes, making the problem-solving of incidents through root cause analysis straightforward. However, the root cause analysis process remains time-consuming. This study employs the Kitchenham standard systematic literature review methodology to explore how information models and deep learning can streamline this process. By conducting a comprehensive search across four major databases, we evaluate the current technological advancements and their application in root cause analysis. The aim of this study is to assesses the impact of information models for root cause analysis in a production environment. Our findings reveal that knowledge graphs, association rule mining, and deep learning algorithms are at the forefront of root cause analysis technology. Notably, the use of neural networks has become increasingly prominent in recent literature, offering significant improvements in analyzing complex data sets. These technologies facilitate a more nuanced and faster analysis by leveraging large-scale data integration and automated learning capabilities. We establish that the integration of these advanced technologies not only speeds up the root cause analysis process but also enhances the accuracy and depth of analysis, surpassing traditional methods that rely heavily on manual interpretation. The effective implementation of these technologies requires a robust foundation of clean, standardized data, giving rise to the concept of "Production IT." Furthermore, it is crucial for this data to be openly available to facilitate academic research, thereby enabling the development of new methods for more efficient and effective root cause analysis.

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Section: Discussionmentioning

confidence: 99%