Cloud computing in a machine automation application
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Automation systems have evolved from local control systems to widely distributed, networked and complex beings. Distributing computing tasks to a number of individual computing units has changed the way automated systems function. Offloading demanding computing to nearly infinitely powerful cloud environments has introduced potential in reducing upfront hardware costs, system updating complexity and energy consumption. The use of cloud resources as a part of hard real-time machine control tasks has been researched in a number of studies. The use of the current cloud technologies has been found feasible in high-level supervisory control tasks, for example. In automation systems, individual sensors and actuators can now have internet access (the Internet of Things, IoT), which enables data gathering to the cloud directly from the devices. In the cloud, vastly complex sensor data-based computing can be executed to gain insights of the automated system or to enhance its performance. This thesis is about creating an infrastructure for gathering sensory data to the cloud and enabling cloud computing in a machine automation application. The cloud resources are provisioned from the public cloud service provider Microsoft Azure and are studied from a functional viewpoint. As the focus is on the functionality of an end-to-end IoT system, intricate cyber security issues are out of the scope of this thesis. The designed solution components are selected and brought together in a case study involving a flexible hydraulic manipulator system and its local control unit. The communication with the cloud and the cloud computing performance were tested, providing information about the applicability of the cloud-based system. In the tests conducted on the proposed system, the communication delays introduced by the wide area network between the local system and the cloud were between 40 and 60 milliseconds. Within this time period, a sensor data packet travelled over the network to the cloud, computations were performed on it and a confirmation message travelled back to the original sender. The obtained results also show that the designed system can support up to 500 Hz sensor data ingestion in the cloud. A cloud extension to an existing system could be made with a very low cost. For the system proposed in this thesis, the upfront hardware costs were about 30€. Additionally, about a 28€ invoice was paid monthly for the used cloud resources.