Oil & gas Consequently, building such systems
Computational costs - training complex AI/deep-learning models for multi- dimensional data is resource-intensive and requires scale-up server configuration. This problem escalates in online learning systems, where models are updated continuously as more data streams become available.
Infrastructure - the data-driven approach from raw input data to predictive outputs requires a robust hardware infrastructure throughout the entire system’s pipeline to perform high- speed processing of large volumes of information. This is because intensive convolution operations and fully connected layers require efficient memory communication between both graphics processing units and central processing units. In addition, optimisation strategies should be in place to distribute the workload of a program among different hardware resources.
requires diverse skill sets, domain-specific knowledge, and a powerful processing unit to produce reliable analytics. TÜV SÜD National Engineering Laboratory has therefore undertaken research to develop applications and device specific data-driven models which can provide accurate predictions as to the state of a given system. In summary, TÜV SÜD has demonstrated the importance of spatial and temporal feature encoding in the predictive accuracy of multisensor systems. The encoded information was then related to historical system conditions to eventually predict the system’s state in the future. The learned features could better represent complicated nonlinearity, inter- sensor relationships and system dynamics. Such prediction outcomes can benefit domain experts in the oil and gas industry as well as other industrial sectors, to ensure optimal equipment uptime and
minimise unplanned events. This also includes real-time flow measurement verification where the CBM software processes data streams in real-time, detects any likely deviations from normal patterns and alerts the operators of a failure’s cause.
TÜV SÜD National Engineering Laboratory
www.tuvsud.com/en-gb/nel
By Behzad Nobakht, data scientist at TÜV SÜD National Engineering Laboratory
Instrumentation Monthly October 2022 47
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