Abstract
Factories in Industry 4.0 are growing in complexity due to the incorporation of a large number of Cyber-Physical System (CPSs) which are logically and often physically distributed. Traditional monolithic control and monitoring structures are not able to address the increasing requirements regarding flexibility, operational time, and efficiency as well as resilience. Self-Aware health Monitoring and Bio-inspired coordination for distributed Automation systems (SAMBA) is a cognitive application architecture which processes information from the factory floor and interacts with the Manufacturing Execution System (MES) to enable automated control and supervision of decentralized CPSs. The proposed architecture increases the ability of the system to ensure the quality of the process by intelligently adapting to rapidly changing environments and conditions.
Highlights
Efficient production requires high degrees of flexibility, adaptiveness, and responsiveness in order to achieve high quality and versatility of manufacturing processes [1]
The optimal reaction should be decided with respect to the system goals, which may themselves change during the operation
It is designed to operate as a middleware on the top of existing systems adding a layer of intelligence to the Cyber-Physical Production System (CPPSs)
Summary
Efficient production requires high degrees of flexibility, adaptiveness, and responsiveness in order to achieve high quality and versatility of manufacturing processes [1]. An important task is the continuous measurement of individually varying product properties in early stages [4] In view of these requirements, researchers have proposed various methods of intelligent sensing, self-organization, and selfoptimization and a number of sophisticated cognitive architectures [5, 6]. Even though the developments of technology have improved the robustness and resiliency of Cyber-Physical Production System (CPPSs) in comparison to conventional automation systems, new expectations such as self-diagnosis and prognosis, self-repair, selfdiscovery and self-configuration, predictability as well as safety [7] are rising We categorize these expectations into three different challenges. 4, it goes through an exemplary scenario with disturbances to elaborate how the system handles the problem and presents a high-level simulation of the system using IEC 61499 function blocks It discusses potential benefits of the proposed architecture and draws conclusions
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