Derivation of Diagnostic Models Based on Formalized Process Knowledge

Arroyo, Esteban; Schulze, Denis; Christiansen, Lars Breum; Fay, Alexander; Thornhill, Nina F.

doi:10.3182/20140824-6-za-1003.00597

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…Going one step further, semantic description and process modeling could be used for an easier if not automated feature engineering. Some process modeling techniques are even sophisticated to generate PLC code automatically [50][51][52]. All of these ideas are not new, but, in particular, discrete manufacturing is very different in terms of process models opposed to continuous processes like chemical production [31].…”

Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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Much research is done on data analytics and machine learning for data coming from industrial processes. In practical approaches, one finds many pitfalls restraining the application of these modern technologies especially in brownfield applications. With this paper, we want to show state of the art and what to expect when working with stock machines in the field. The paper is a review of literature found to cover challenges for cyber-physical production systems (CPPS) in brownfield applications. This review is combined with our own personal experience and findings gained while setting up such systems in processing and packaging machines as well as in other areas. A major focus in this paper is on data collection, which tends be more cumbersome than most people might expect. In addition, data quality for machine learning applications is a challenge once leaving the laboratory and its academic data sets. Topics here include missing ground truth or the lack of semantic description of the data. A last challenge covered is IT security and passing data through firewalls to allow for the cyber part in CPPS. However, all of these findings show that potentials of data driven production systems are strongly depending on data collection to build proclaimed new automation systems with more flexibility, improved human–machine interaction and better process-stability and thus less waste during manufacturing.

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Section: Semantic Description Of Available Datamentioning

confidence: 99%

Data Science on Industrial Data—Today’s Challenges in Brown Field Applications

2021

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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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