Multi-Objective Design Space Exploration for the Integration of Advanced Analytics in Cyber-Physical Production Systems

The integration of advanced data analytics in manufacturing systems has shown impressive results in various fields, including fault diagnosis, predictive maintenance, energy management, and manufacturing system control. However, due to the distributed nature of analytics algorithms and the growing complexity of modern production systems, the performance and the cost of such systems highly depends on the underlying system architecture. Therefore, it is mandatory that system architects systematically explore and evaluate all architectural alternatives of the highly constrained design space defined by the systems functional and economical objectives. This paper presents a design-space-exploration method that not only generates different implementation alternatives, but also provides a formal performance analysis of the generated solutions. By analyzing the architecture of a manufacturing system as well as the data flow graph model of a data analytics algorithm, we automatically allocate, synthesize, and generate different simulatable software solutions to efficiently compute and visualize data analytics algorithms on the shop floor. This approach allows the user to evaluate different architectural implementation during the design phase, to select a solution according to its requirements and to analyze the performance of the resulting system. The applicability of this method is also demonstrated by means of a real world example.