Abstract
Industrial applications are in transition towards modular and flexible architectures that are capable of self-configuration and -optimisation. This is due to the demand of mass customisation and the increasing complexity of industrial systems. The conversion to modular systems is related to challenges in all disciplines. Consequently, diverse tasks such as information processing, extensive networking, or system monitoring using sensor and information fusion systems need to be reconsidered. The focus of this contribution is on distributed sensor and information fusion systems for system monitoring, which must reflect the increasing flexibility of fusion systems. This contribution thus proposes an approach, which relies on a network of self-descriptive intelligent sensor nodes, for the automatic design and update of sensor and information fusion systems. This article encompasses the fusion system configuration and adaptation as well as communication aspects. Manual interaction with the flexibly changing system is reduced to a minimum.
Highlights
Sensors and actuators, and other sources such as databases serve as data sources for the realisation of condition monitoring in industrial applications or for the acquisition of characteristic parameters, such as production speed or rejection rate
This article proposes an approach to the automated design of adaptable SEFU/IFU systems
Semantic data is exchanged by a middleware
Summary
Other sources such as databases serve as data sources for the realisation of condition monitoring in industrial applications or for the acquisition of characteristic parameters, such as production speed or rejection rate. Modern industrial plants are equipped with an increasing number of sensors generating a large amount of data. The task of processing these large amounts becomes increasingly complex. Machine operators are unable to properly process and draw correct conclusions from the generated information [1]. The issues caused by the increased complexity are addressed by Sensor and Information Fusion (SEFU/IFU) mechanisms. These collect and combine data and information from different sources to reduce complexity as well as uncertainty. The idea of SEFU/IFU is to create new or more precise knowledge about the system’s environment or status (such as physical quantities or occurring events) by taking into consideration different information sources [2]
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