Lean Data Services: Detection of Operating States in Energy Profiles of Intralogistics Systems by Using Big Data Analytics
Due to the rising energy costs and the increasing competition on the market the improvement of the energy efficiency in production systems can be a great chance for companies to gain an advantage compared to their competitors. Therefore, the transparency of the energy consumption of the several processes of such systems has to be known in detail. In this paper a method for the detection of operating states in production systems, based on big data analysis is introduced. The developed algorithm automatically detects different operating states in the current profile and improves his accuracy the longer it is used. To evaluate the developed data analysis algorithm, a defined process with a conveyor belt was considered and measured several times. The algorithm defines clusters from the measurements and identifies classes for the several operating states. With the naive Bayes classifier it is possible to categorize the clusters more fine-grained and faster than with a correlation function. An advantage of the method is that each new classified instance is taken into account for future unclassified instances. So it is possible to integrate a continuous learning process in production processes and to consider a slowly drift of states. Also, the subsequent addition of classes and attributes that are represented in this work by clusters, is possible at any time.
Jörg Franke and Sven Kreitlein
C. Oette et al., "Lean Data Services: Detection of Operating States in Energy Profiles of Intralogistics Systems by Using Big Data Analytics", Applied Mechanics and Materials, Vol. 856, pp. 73-81, 2017