Data science on industrial data -- Today's challenges in brown field applications
It identifies key barriers for practitioners in industrial settings, making it an incremental contribution focused on real-world implementation rather than novel methods.
The paper addresses the practical challenges of applying data science and machine learning to industrial brown field applications, highlighting issues such as cumbersome data collection, poor data quality, lack of semantic descriptions, and IT security constraints.
Much research is done on data analytics and machine learning. In industrial processes large amounts of data are available and many researchers are trying to work with this data. In practical approaches one finds many pitfalls restraining the application of modern technologies especially in brown field applications. With this paper we want to show state of the art and what to expect when working with stock machines in the field. A major focus in this paper is on data collection which can be more cumbersome than most people might expect. Also data quality for machine learning applications is a challenge once leaving the laboratory. In this area one has to expect the lack of semantic description of the data as well as very little ground truth being available for training and verification of machine learning models. A last challenge is IT security and passing data through firewalls.