Figure 3: Survival Probability Comparison of three different flow meters installed in the same erosive flow conditions.

actionable intelligence to end-users without the need to be an expert in data science. Both windows were designed to meet specific customer requirements for data visualisation. As the name would suggest, the accuracy of

data-driven modelling improves with the more data you feed it. This is where historical data can play a vital part in the process. While of course end-users will expect their CBM system to act on live data, data scientists can use archived data dating back years from a given engineering plant to increase the model’s training dataset resolution and effectively backdate the model’s awareness of facility operation with information pertaining to previous faults and instrument calibration drift. The model will therefore be able to flag similar events should they occur again in the future. Taking it one step further, by incorporating

measurement uncertainty analysis into CBM, this improves predictive performance even when the live data departs from the initial training

data. However, to date, little consideration has been given to uncertainty quantification over the prediction outputs of such models. It is vital that the uncertainties associated with this type of in-situ verification method are quantified and traceable to appropriate flow measurement national standards. Therefore, research in this field is currently

underway at TÜV SÜD National Engineering Laboratory, the UK’s Designated Institute for fluid flow and density measurement. By utilising our flow loops to generate diagnostic datasets representative of field conditions, multiple CBM models can be trained by the in-house Digital Services team. In this controlled environment, every instrument’s measurement uncertainty is understood and accounted for in the CBM model. The end goal of CBM research is to obtain data-driven models which are highly generalisable and capable of interpreting live field datasets. By quantifying the uncertainty

associated with model outcomes this will create a National Standard for remote flow meter calibration, with the knowledge and experience of current practices built in. Where CBMs were maybe once seen as a

novelty or ‘nice to have’ in the eyes of end- users, the need for cost saving and increased operational efficiency in industry has never been more prevalent. Successfully implemented/well trained CBM systems can help operators realise these goals. For example, instead of an oil rig shutting down production to remove a flow meter from a pipeline, box it up, ship it to a calibration lab, calibrate it and then re-ship and reinstall once a year (where there are considerable costs associated with each stage), a CBM system can simply tell you that based on the live and historical data, the meter performance has not drifted in two years and therefore production can continue. Or if an operator observes that the flow rate readings have in fact started to deviate, a well-trained CBM system might be able inform them that the problem does not lie with a drifting meter calibration, the deviation can instead be attributed to the erosion of the meter due to previously undetected particles in the flow and that immediate intervention is required to prevent permanent damage. This early knowledge of a developing issue

can save the operator thousands of pounds in replacement meter costs and manual fault diagnosis time. In summary, as more and more end-users upgrade their plants to take advantage of digital instrumentation, the relevance and uptake of CBM models will continue to grow to the point where they are no longer a novelty but a necessity.

TÜV SÜD National Engineering Laboratory

44 March 2021 Instrumentation Monthly

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