Approach for Risk Identification and Assessment in A Manufacturing System

The growing number of sensors in production systems increases the availability of data for a manufacturing system. This data can be employed to recognize process related, operative risks during the production process more precisely and estimate the risk level of a factory. This renders the possibility to reduce possible risks, like machine breakdown or tool failure, even before their occurrence. We therefore present an approach of risk identification of a production system based on sensor induced events from the shop floor and a possible evaluation scheme of such risks. The described case study demonstrates the feasibility of the approach

Integrated Production and Maintenance Planning for Cyber-physical Production Systems

Continuously rising production costs and efficiency requirements present challenges for manufacturing companies. One way of overcoming these challenges is to improve maintenance efficiency and effectiveness by developing and integrating predictive maintenance tools, and using this information for the targeted planning of maintenance measures. However, the integration of sensors into previously-installed manufacturing resources for predicting the necessary maintenance tasks is one of the main challenges facing manufacturing companies. Therefore, this paper presents an innovative methodology for predictive maintenance tools as intelligent cloud services and the industrial application of this methodology for an integrated production and maintenance planning

Vorausschauende Instandhaltung für Fertigungsressourcen: Vorgehensmodell zur Integration von Predictive-Maintenance-Werkzeugen in Fertigungsressourcen

Increasing requirements for machine reliability and availability lead to an increased importance of predictive maintenance tools. How-ever, conventional manufacturing resources are not equipped with a sufficient number of sensors to monitor the machine condition and predict necessary maintenance measures. This paper presents a procedure for integrating additional sensors into existing manufacturing resources for predictive maintenance tools

Knowledge-based decision making in a cyber-physical production scenario

Market requirements such as shortened product life cycles as well as the increasing number of variants up to customized products lead to more complexity in production systems. Digitalization initiatives, such as the vision of “Industrie 4.0”, try to cope with this complexity by means of smart products and smart machines that are equipped with their own decision making capabilities for steering flexible ad-hoc production processes. In this paper, we discuss the use of semantic technologies together with cyber-physical systems for integrating decision making into smart production machinery. We report on the experiences with a prototypical realization based on Semantic Web technology on top of a complex cyber-physical production demo system at the Fraunhofer IGCV in Augsburg