Chemical Engineering September 2020 - 32

Feature Report
A Holistic Approach for
Pump-System Analytics
A step-by-step approach is presented to address the modern requirements of a
pump-system digital solution
Stylianos Giannoulakis
Sulzer Management Ltd.
I
n recent years, significant developments
have been achieved in
the fields of cloud computing and
machine learning techniques. In
conjunction with increased awareness
regarding the benefits of digital
transformation, from both users
and manufacturers of industrial
equipment, a unique opportunity is
emerging. This article describes a
comprehensive solution for addressing
pump system monitoring, and
predictive and optimization analytics
in a holistic manner. The target of the
proposed approach is to leverage all
available system data and infrastructure
and to address issues of traditional
monitoring solutions. Typically,
traditional performance monitoring
and predictive solutions are simply
accepting errors of raw sensor data
or modeling assumptions, or both.
In addition, any reliability alerts are
based on thresholds of individual
sensors, which are usually triggered
at the time of failure, providing no reaction
time to the user.
As shown in Figure 1, five distinct
steps are defined as part of an integrated
solution: 1) connect remote
industrial equipment to the internet;
2) monitor the state of the equipment;
3) analyze the operating conditions;
4) predict the behavior of individual
components and of the complete
system by training customized models;
and 5) optimize a pump system
at the aspects of performance efficiency,
reliability and profitability.
This article presents the background
of the successful deployment of the
first three steps to a major pipeline
operator, along with the current re32
search
and development efforts to
cover the remaining two steps.
Connect
Connectivity
Different connectivity options are
available today, however the preferred
solution
will
exploit
existing
infrastructure. Typically, most pump
systems of medium or high criticality
are equipped with a certain number
of permanent sensors, registering
values of fluid volumetric flow, pressures,
density, pump status, vibrations,
temperatures and driver energy
consumption. All sensor values
are stored in a central local database
and can be accessed by the operator.
The proposed solution extends
the process by fetching all required
values from the local database and
transferring it to a cloud solution (Figure
2). This approach leverages existing
infrastructure, does not disrupt
existing operations and delivers all
necessary data to a cloud environment,
which offers global access to
multiple users and leverages modern
techniques of cloud computing.
Raw sensor data are averaged for
a short pre-defined time interval (typically
in the range of a few minutes)
and a selection of monitored parameters
are concentrated into a file,
which is subsequently pushed to a
secure cloud service. The data transfer
is only one way, enabling the user
Data
push
Customer
firewall
Queue
Monitor
Collect all data in the
central cloud database
for cloud computing
Define the state of
every component at
every moment
Analyze
Evaluate impact of
historical operation
to performance and
reliability
Predict
Simulate operating
conditions and predict
imminent failures
Optimize
Minimize energy
consumption and
maximize reliability
index
Figure 1. Visual representation of a step-by-step
approach for pump system analytics
to push the required data, but at the
same time ensuring that no access
is possible through the firewall. Each
file pushed from the user includes
sensor values as a representation
of the equipment's operating conditions
for a discrete timestamp. All
Workers
Cloud
services
Cloud
database
Figure 2. This diagram shows the automated data flow of asset-sensor values to the cloud infrastructure
for further evaluation
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Chemical Engineering September 2020

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