BMTA Newsletter BMTA Newsletter - Spring 2020 | Page 10
bmta.co.uk
PREDICTIVE MODEL DEVELOPMENT
Dr Yanfeng Liang
Mathematician
TÜV SÜD National Engineering Laboratory
UNLOCKING THE POTENTIAL IN LARGE HISTORICAL DATA SET
In a world where data is coming in large volumes and fast speeds, without the use of proper advanced modelling techniques
such as machine learning models, the value and benefits from data cannot be optimised. In flow measurement, flow meters are
capable of outputting digital data sets, which can be used to indicate the performance of flow meters under different operating
conditions. Errors such as improper installations, deposition and the presence of a second phase can be predicted using
predictive models to enable condition-based monitoring and increase the industry’s efficiency in fault diagnosis process. Thus,
reducing operational cost.
As the world is moving towards digitalisation, it has become increasingly important to utilise predictive models and machine
learning algorithms to extract valuable information and obtain new insights from the available data. In an industry such as flow
measurement, vast amounts of data are stored and generated by flow meters which have built-in digital transmitters outputting at
a high frequency. This output contains valuable information on the performance of flow meters as well as their operating
conditions. In other words, big data can be used to enable predictive maintenance and condition-based monitoring which will
effectively reduce operating costs and improve the decision-making process. However, storing data alone is not enough to
unlock these opportunities. Data is only useful if appropriate modelling strategies are used to extract the underlying values and
obtain new insights.
TÜV SÜD National Engineering Laboratory holds the UK’s national standard for measurement in density and flow. Over the
years, their data acquisition systems have logged and archived 20 years’ worth of data detailing various flow meters’
performance, test facility configuration and operating conditions. It was observed, from multiple research projects conducted
over the years, that any error such as improper meter installation, deposition such as wax and the presence of a second phase
are manifested through drifts in a meters’ diagnostic variables.
However, interpreting the data can be challenging as different errors can induce the same drift patterns in the same diagnostic
variables. Consequently, it becomes extremely difficult for end-users to distinguish between different errors using basic visual
observation tools. This increases fault diagnosis time and could delay rectification actions which ultimately impact the reliability
and accuracy of the primary measurements generated by the flow meters.
With more and more data becoming available, complex issues arise such as high dimensionality data (dataset with a large
number of variables) as well as poorly structured databases with missing data labels. Diagnostic variables from flow
measurement often have interrelationships with each other which increases the difficulty in analysing the underlying relationships
between variables. Furthermore, in flow measurement, certain experiments can be expensive to conduct, resulting in a limited
amount of data. For example, failure data on flow meters as well as erosion data are limited due to the fact that such tests are
extremely costly to conduct and cause significant damage to the meters.