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Calibration


Further elevating condition-based monitoring calibration


Traditionally, flow meter calibration and maintenance plans have operated on a calendar-based timescale. This sees meters that are exemplifying no problems being unnecessarily taken out of service for scheduled maintenance, wasting time, effort and money. In contrast, meters or instruments that do need action taken before the period ends are operating with errors for unknown periods of time. Here, Gordon Lindsay, technical lead at TÜV SÜD National Engineering Laboratory, discusses how real- time prediction modelling can be used to develop real-time


predictive techniques, monitoring normal/abnormal changes throughout the lifetime of flow meters. Ultimately, these data-driven models deliver accurate, real-time prediction of flow meter performance and qualify uncertainty.


A major driver for this has been the fact that end-users now have a wealth of diagnostic data available to them from digital transmitters fitted to control and instrumentation devices installed throughout facilities. The data can be accessed in real time through OPC (Open Platform Communications) servers or stored in a database for future analysis. Through data- driven modelling, this data can be used to replace inefficient ‘time-based’ calibration and maintenance schedules with ‘condition-based’ monitoring (CBM) systems which can remotely determine facility process conditions, instrument calibration validity and even measurement uncertainty without the need for unnecessary manual intervention which is costly and time consuming for operators. By measuring the real-time status of facility


D


and calibration conditions, CBM is also capable of uncovering hidden trends and process value correlations which were otherwise undetectable to standard human-based observations. The information generated


42


igitalisation strategies are now commonplace throughout the manufacturing and engineering sectors.


through CBM can be used to predict component failure, detect calibration drift, reduce unscheduled downtime and ultimately provide a framework for in-situ device calibration and verification. As such, many engineering consultancies now


offer ‘Digital Services’ in which they offer to build CBM systems based on a technique known as data-driven modelling. This enables the system to automatically flag anomalies and classify undesirable operating conditions to operators by using the time-series data output by the digital transmitters installed throughout the plant. When specifying such a system with a


consultancy, it is important to realise that there is no true ‘one size fits all’ solution. Every facility is different, from its mechanical build (e.g. pipe bends, valve positions) to its instrumentation (IO count, number of pressure/temperature sensors), not to mention the subtle variations between the digital data that can be obtained from differing manufacturers of the same instrument. Therefore, these models are not trained blindly, and the data scientists employed by such consultancies will work with the experienced on-site plant designers


Figure 1: Data Science Workflow at TÜV SÜD National Engineering Laboratory


March 2021 Instrumentation Monthly


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