With increasing digitization, models are more important than ever. Especially their use as soft sensors during operation offers opportunities in cost saving, easy data acquisition and therefore additional functionality of systems. In soft sensor networks there is redundant data acquisition and consequently the occurrence of inconsistent values from different soft sensors is encouraged. The resolution of these data-induced conflicts allows for the detection of changing components characteristics. Hence soft sensor networks can be used to detect wear in system components. In this paper this approach is validated on a test rig. It is found, that the soft sensor network is capable to determine wear and its extent in eccentric screw pumps and valves via data induced conflicts with relatively simple models.

The UAE Pavilion will be a major attraction at Expo 2020 in Dubai. The roof of the building consists of 28 operable wings made of carbon and glass fiber, having masses ranging from 5 to 18 tons and total lengths in the range of 30 to 65 m that have to be actuated by a dedicated mechanism. In this paper we present the turn-key project for the design, manufacturing, installation, test and commissioning of the Roof Wing Opening System, which represents a unique system world-wide for operating the wings. It consists of one Hydraulic Power Unit with approximately 1 MW of installed power, 2 km of piping working at the nominal pressure of 210 bar, 46 hydraulic cylinders with 1.5 tons of mass each and the complete automation and control subsystem that includes 9 separate PLCs, dedicated software, 2.000 sensors and control points, and over 20 km of harness. One major challenge is the control of the wings. Part of them, due to their huge dimensions and masses, are actuated using two or three hydraulic cylinders that have to be properly synchronized during the movement, preventing unwanted displacements in order to avoid stresses on the wing mechanical structure and ultimately permanent damages. Due to the nature of the project, a final validation of the control algorithms can be done only at system level during the commissioning phase. Therefore, particular care has to be devoted to the verification strategy, anticipating the behavior of the system in the early validation stages and following a V-model approach, in order to identify critical situations and reduce the overall risk. After a brief system description, we will explain how the verification has been approached by using system level simulations and dedicated testing activities on specific subsystems. In particular, we will detail the verification of the control algorithms that has been performed on a dedicated Hardware-Inthe- Loop system first, followed then by dedicated tests on a reduced wing mock-up, allowing the study of the system behavior under the most critical conditions. These include the application of external forces with specified profiles. Finally, we will provide the actual status of the system installation, testing and commissioning activities that have been running in Dubai since January 2019.

Cyber-Physical System (CPS) or Digital-Twin approach are becoming popular in industry 4.0 revolution. CPS not only allow to view the online status of equipment, but also allow to predict the health of tool. Based on the real time sensor data, it aims to detect anomalies in the industrial operation and prefigure future failure, which lead it towards smart maintenance. CPS can contribute to sustainable environment as well as sustainable production, due to its real-time analysis on production. In this thesis, we analyzed the behavior of a tool of Ringvalsverk 4, at Ovako with its twin model (known as Digital-Twin) over a series of data. Initially, the data contained unwanted signals which is then cleaned in the data processing phase, and only before production signal is used to identify the tool’s model. Matlab’s system identification toolbox is used for identifying the system model, the identified model is also validated and analyzed in term of stability, which is then used in CPS. The Digital-Twin model is then used and its output being analyzed together with tool’s output to detect when its start deviate from normal behavior.