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Calibration


and operators to ensure that the outputs from these models align with the realities of the system and, most importantly, the subtleties of the customer’s engineering environment. A typical data science workflow at TÜV SÜD National Engineering Laboratory is shown in Figure 1. It might seem obvious but defining the


objective(s) of the CBM system is a crucial first step as this will inform which modelling techniques will be used, and allow the data science team and customer to agree upon realistic milestones for the development of the system. For example, the evolution of the system may be split into three stages as the customer’s confidence in the system grows over time.


Stage 1


Detect anomalies in the data streams, e.g. deviations from established baselines in all system process values and associated intercorrelations and flag to end-user as a possible source of flow measurement error.


Stage 2


Not only detect anomalies but detect and classify specific fault conditions and flag to the end-user. E.g. calibration drift in orifice plate’s static pressure sensor, misaligned Coriolis flow meter at location ‘X’.


Stage 3


Quantify the effects of this fault condition on the system’s overall measurement uncertainty with respect to measured mass flow rate.


A first pass data collection and integration


stage is then undertaken. At this point, multiple sources of data are standardised into a singular database which the model will read from. This can sometimes be time consuming as most plants were not necessarily designed with CBM in mind. As such, there may be three different data acquisition (DAQ) systems associated with three corresponding plant sub-systems, which in turn log and store their data in three different file formats, with different labels and units. With the data standardised, ‘exploratory


data analysis’ (EDA) is then possible and is the point where the data science team will seek to uncover patterns and correlations within the data and align them to real world occurrences with input from the experienced plant engineers and operators. ‘Feature engineering’ as it’s known in the data science community, is the process of ranking individual process variable importance with respect to the CBM detecting or predicting the events defined in the project objectives. After these initial steps have been


completed the model development can begin, which in itself is an iterative process, especially in scenarios where the system is to be developed using live data. For example, if one wishes the CBM system to detect unwanted gas entrainment in a fluid flow scenario (which can effect flow meter operation and calibration), then one has to wait for such a situation to occur to then allow the model to learn the cascading patterns now present in the digital data. Figures 2 and 3 are examples of anomaly


detection and failure analysis trend windows which were designed to summarise the complex streams of information output by the CBM model in a format that provides


Continued on page 44...


Figure 2: Anomaly Detection Visualisation. Instrumentation Monthly March 2021 43


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