Context extraction for self-learning production systems

Stokić

2017

Sensors

Self Cite

Highly flexible manufacturing systems require continuous run-time (self-) optimization of processes with respect to diverse parameters, e.g., efficiency, availability, energy consumption etc. A promising approach for achieving (self-) optimization in manufacturing systems is the usage of the context sensitivity approach based on data streaming from high amount of sensors and other data sources. Cyber-physical systems play an important role as sources of information to achieve context sensitivity. Cyber-physical systems can be seen as complex intelligent sensors providing data needed to identify the current context under which the manufacturing system is operating. In this paper, it is demonstrated how context sensitivity can be used to realize a holistic solution for (self-) optimization of discrete flexible manufacturing systems, by making use of cyber-physical systems integrated in manufacturing systems/processes. A generic approach for context sensitivity, based on self-learning algorithms, is proposed aiming at a various manufacturing systems. The new solution encompasses run-time context extractor and optimizer. Based on the self-learning module both context extraction and optimizer are continuously learning and improving their performance. The solution is following Service Oriented Architecture principles. The generic solution is developed and then applied to two very different manufacturing processes.

Section: Methodsmentioning

confidence: 99%

Section: State Of the Artmentioning

confidence: 99%

Holistic Context-Sensitivity for Run-Time Optimization of Flexible Manufacturing Systems

Scholze

Stokić

2017

Sensors

Self Cite

“…These technology approaches were often tried out by using ontologies which are describing the relations and associations of component data in a system. Selflearning context approaches [43] could help in the future to optimize systems automatically in several aspects (energy efficiency, performance, etc.) because it is a mighty methodology to identify in a high detail degree the behavior of systems and can help to predict future situations.…”

Section: Context Awarenessmentioning

confidence: 99%

A survey of service-based systems-of-systems manufacturing systems related to product life-cycle support and energy efficiency

Nagorny

Colombo

2014 12th IEEE International Conference on Industrial Informatics (INDIN)

2014

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Current trends are showing the growing demand of embedded systems, related to cyber-physical systems, which consist of smart physical actors and/or sensors that are reachable e.g. with service-oriented technologies, cooperating and negotiating to behave showing systems-of-systems characteristics and without any defined hierarchical structures. Possible evolvable structures and emergent behaviors allow generating new features/functions by the collaboration of different components of the cyber-physical systems. These Systems-of-Systems characteristics of service-based cyberphysical systems used among others for manufacturing processes are very suitable to support the product life-cycle in the initial (production) phase of a product and are very suitable to increase the energy efficiency of a whole manufacturing system using context sensitive technologies during the running life cycle phase. This paper presents a brief state-of-the-art analysis for servicebased manufacturing systems related to product life-cycle support in the initial phase and energy efficiency aspects.

“…The Self-Learning approach and methodology intends to enhance traditional monitoring and control solutions by relying context awareness [9] and data mining techniques in order to select the optimum set of manufacturing process parameters for each operative context. Thus, a self-learning production system (SLPS) will be able to both perceive the context in which manufacturing process is operating and suggest the best set of manufacturing process parameters by using the relationship between the performance of the process and its controllable input parameters.…”

Section: Introductionmentioning

confidence: 99%

Self-Learning Production Systems (SLPS) - Optimization of manufacturing process parameters for the shoe industry

Orio

Cândido

2013 11th IEEE International Conference on Industrial Informatics (INDIN)

et al. 2013

Self Cite

The manufacturing processes of today are caught between the growing needs for quality, high process safety, efficiency in manufacturing process, reduced time-to-market and higher productivity. In order to meet these demands, more and more manufacturing companies are betting on the application of intelligent and more integrated monitoring and control solution to reduce maintenance problems, production line downtimes and reduction of manufacturing operational costs while guarantying a more efficient management of the manufacturing resources. In this scenario, the research currently done under the scope of the Self-Learning Production Systems (SLPS) tries to fill these gaps by providing a new and integrated way for developing monitoring and control solutions based on novel technologies and especially on self-adaptive, context awareness and data mining techniques. This paper introduces the research background that has driven the design of the generic SLPS architecture and focuses on the Adapter component responsible for adapting the system behaviour according to the actual operative context. The proposed Adapter architecture together with its core components are introduced as well as the generic adaptation process, or rather, the way the Adapter adapt the system behaviour to cope with the current context. Finally, to demonstrate the applicability of the SLPS methodology into real industrial context as well as the Adapter capabilities to learn and evolve along system lifecycle an application scenario is presented.