Industrial Cyber-Physical Systems consist of multiple machines working together and demand efficient and flexible communication methods to function as intended. The protocols used in industrial operations and web applications are often contradictory in regards to the latency and security characteristics. Due to these differences, the intersection of operation and information technologies is a challenging area. But the rewards in smoother information flow are also high, providing a fruitful area for development. This paper introduces a general wrapper application to enable the use of the industrial OPC UA server through an interface implemented with web technology GraphQL. The results demonstrate sufficient performance for the middleware to be used in an overhead crane control application, bringing the agility of web development to industrial environments.
Industry 4.0 and Cyber-physical systems require easy access to shop-floor data, which allows the monitoring and optimization of the manufacturing process. To achieve this, several papers have proposed various ways to make OPC UA (Open Platform Communications Unified Architecture), a standard protocol for industrial communication, RESTful (Representational State Transfer). As an alternative to REST, GraphQL has recently gained popularity amongst web developers. This paper compares the characteristics of the REST and GraphQL interfaces for OPC UA and conducts measurements on reading and writing data. The measurements show that GraphQL offers better performance than REST when multiple values are read or written, whereas REST is faster with single values. However, using OPC UA directly outperforms both REST and GraphQL interfaces. As a conclusion, this paper recommends using a GraphQL interface alongside an OPC UA server in smart factories to simultaneously yield easy data access, the best performance, and maximum interoperability.
With the emerging role of digitalization in the industrial sector, more and more companies attempt to increase asset availability, improve product quality and reduce maintenance costs. Manufacturing companies are faced with the need to transform traditional services into remote factory monitoring solutions using big data and advanced analytics. Kone is a global leader in the elevator and escalator production industry, which is continuously looking for new ways of improving production efficiency and reducing machine downtime in order to run unmanned 24/7 production. However, the process of collecting data from equipment and utilizing it for predictive analytics can be challenging and time consuming. Therefore, during Serena project Kone cooperated with VTT and Prima Power, which provided necessary capabilities and competencies in the areas of data collection, analysis and utilization for developing and testing predictive maintenance solutions in the elevator manufacturing industry. As a result of this collaboration, VTT integrated sensors into Prima Power production line used at Kone and developed algorithms for measuring the remaining useful life of conveyor bearings. As a machine tool builder, Prima Power contributed to the project with a cloud environment for remote collection of vibration measurement data and Serena Customer Web analytics for condition-based maintenance.
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