Next Article in Journal
A Model for the Spread of Infectious Diseases with Application to COVID-19
Previous Article in Journal
Healing Anthropocene Syndrome: Planetary Health Requires Remediation of the Toxic Post-Truth Environment
Review

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

Fraunhofer Institute for Process Engineering and Packaging (IVV), Division Machinery and Processes, Heidelberger Str. 20, 01189 Dresden, Germany
*
Author to whom correspondence should be addressed.
Challenges 2021, 12(1), 2; https://doi.org/10.3390/challe12010002
Received: 24 September 2020 / Revised: 15 January 2021 / Accepted: 20 January 2021 / Published: 25 January 2021
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. View Full-Text
Keywords: industrial communication; industrial informatics; cyber-physical production system; machine to machine communication; OPC UA industrial communication; industrial informatics; cyber-physical production system; machine to machine communication; OPC UA
Show Figures

Figure 1

MDPI and ACS Style

Klaeger, T.; Gottschall, S.; Oehm, L. Data Science on Industrial Data—Today’s Challenges in Brown Field Applications. Challenges 2021, 12, 2. https://doi.org/10.3390/challe12010002

AMA Style

Klaeger T, Gottschall S, Oehm L. Data Science on Industrial Data—Today’s Challenges in Brown Field Applications. Challenges. 2021; 12(1):2. https://doi.org/10.3390/challe12010002

Chicago/Turabian Style

Klaeger, Tilman; Gottschall, Sebastian; Oehm, Lukas. 2021. "Data Science on Industrial Data—Today’s Challenges in Brown Field Applications" Challenges 12, no. 1: 2. https://doi.org/10.3390/challe12010002

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop