Department of Mechanical and Mechatronic Engineering

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Browsing Department of Mechanical and Mechatronic Engineering by browse.metadata.advisor "Bekker, A."

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    Digital twin paradigms towards monitoring insights for deep aquifer pumps
    (Stellenbosch : Stellenbosch University, 2023-12) Anker, Carike; Bekker, A.; Stellenbosch University. Faculty of Engineering. Dept. of Mechanical and Mechatronic Engineering.
    ENGLISH ABSTRACT: Digital twins promise improved decision support by entangling sensor feeds from real assets with digital models to inform on the asset state and behaviour. By leveraging this entanglement, fault detection and diagnosis can be augmented. Digital means can increase the “visibility” of underground water infrastructure through immersive visualisations and virtual sensors. Data-driven monitoring and anomaly detection could assist in improved management of unwanted emergent asset behaviour. The potential value of digital twin paradigms in monitoring deep aquifer water abstraction systems is evaluated within the context of a case study on a series of borehole pumps. Five of the progressive cavity pumps failed shortly after installation, with no meaningful variation in the control data to prompt the onset of any anomalies. The failures point out that human operators could be assisted through vigilant tools that effectively monitor the state of the mechanical equipment. Though a significant amount of data is being collected by the existing control and monitoring system, it became clear that the presentation of this data to operators was not conducive to predicting failure, or even identifying faulty behaviour. The question arises if the existing control measurements may deliver additional value with regard to fault and failure detection if digital models and/or additional sensors were added with well-considered design intent. Through an in-depth analysis of available historical operational data, the possibility of using the current control data compared to a virtual model of expected behaviour and performance to warn of faults or failure is explored. Improved understanding of the effects of operation under particular circumstances can assist operators in identifying when a pump is operating abnormally and possibly predict failure. To optimise the value of the monitoring tool, preference is given to using the existing measurements more effectively, rather than adding additional sensors. A real-time monitoring tool is developed based on the analysis of the historical operational data that places the operation of each pump in the context of its expected performance. The expected performance is quantified from various sources, including the published pump performance curves, the design duty point of each pump, some physics-based performance models, and the expected movement of the measured parameters in relation to one another. The monitoring tool takes the form of a dashboard that reads the data in real time as it is published by a simulated pump control system, and presents the current performance of the pump in the context of its expected performance. This greatly simplifies the task of detecting anomalous operation as opposed to the current monitoring system, which relies on operators to identify concerning behaviour from absolute values and limited trending of measurements over time. Thus, some “digital visibility” of the operation of the pump is granted, and operators have access to additional insight concerning the state of the asset. Applying digital twin paradigms to enhance monitoring of the underground infrastructure presents the opportunity to warn of operational anomalies and provide decision support for the necessary maintenance.