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

Abstract

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.