sensors
Article
An Industrial Digitalization Platform for Condition
Monitoring and Predictive Maintenance of
Pumping Equipment
Michael Short 1, *
1
2
*
and John Twiddle 2
School of Science, Engineering and Design, Teesside University, Middlesbrough TS1 3BA, UK
Scottish & Southern Energy Ltd., Knottingley, West Yorkshire WF11 8SQ, UK
Correspondence: m.short@tees.ac.uk
Received: 12 June 2019; Accepted: 28 August 2019; Published: 31 August 2019
Abstract: This paper is concerned with the implementation and field-testing of an edge device
for real-time condition monitoring and fault detection for large-scale rotating equipment in the
UK water industry. The edge device implements a local digital twin, processing information from
low-cost transducers mounted on the equipment in real-time. Condition monitoring is achieved
with sliding-mode observers employed as soft sensors to estimate critical internal pump parameters
to help detect equipment wear before damage occurs. The paper describes the implementation
of the edge system on a prototype microcontroller-based embedded platform, which supports the
Modbus protocol; IP/GSM communication gateways provide remote connectivity to the network core,
allowing further detailed analytics for predictive maintenance to take place. The paper first describes
validation testing of the edge device using Hardware-In-The-Loop techniques, followed by trials
on large-scale pumping equipment in the field. The paper concludes that the proposed system
potentially delivers a flexible and low-cost industrial digitalization platform for condition monitoring
and predictive maintenance applications in the water industry.
Keywords: industrial digitalization; industry 4.0; sliding mode observers; soft sensors; edge
computing; condition monitoring; predictive maintenance; field testing; IoT
1. Introduction
With improved connectivity and dramatically increased access to low-cost computational power,
many industries are currently on the verge of a second digital revolution known as ‘Industry 4.00 [1].
Of the many advantages that can potentially be leveraged by Industry 4.0, the promise of increased
integration of the real-time control, monitoring, and operational aspects of equipment in the process
industries is one of the most attractive [1,2]. It has previously been suggested that one of the so-called
grand challenges which can be addressed by digitization is the notion of the ‘sustainable, 100%
available plant’ [1]. The 100% availability notion captures the vision that in the future there will be
only ever be planned maintenance stops; while clearly never achievable in practice, the right balance
between maintenance cost and risk must clearly be aimed for. At the heart of this aspect of industrial
digitalization the topics of real-time Condition Monitoring (CM) and Predictive Maintenance (PM) can
be found [1,2]. As with other Internet-of-Things and Industry 4.0 applications, CM/PM applications
can potentially generate large volumes of high-velocity real-time data; therefore, it is beneficial to run
local processing of this data at the edge of the network (on an ‘edge device’), to reduce the frequency
and real-time requirements of communication to core devices [3]. In addition, since it is essential to
have adequate fault indices that could serve as indicators in order to predict early failures and create
an effective PM schedule [2], in an Industry 4.0 context an appropriate approach would seem to be to
distribute the tasks of CM/indices generation and PM analytics between the network edges and Core.
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To this end, this paper is concerned with the development, validation and field-testing of a
real-time CM/PM system for deployment upon large-scale pumping equipment in the water industry
in an Industry 4.0 context. The CM and fault detection is achieved with an edge device implementing
sliding-mode observers as soft sensors, which are designed to monitor equipment locally in a water
pumping station. The role of the observers is to detect equipment wear - from mechanical stresses,
or the ingress of coolant or other particulates into coupling bearing support housing - before further
damage occurs to the equipment. It achieves this by monitoring several low-cost transducer signals
from the equipment, and building a digital twin of the equipment being monitored; including the
estimation of two critical internal parameters of the rotating equipment (bearing friction factor and
coolant flow) which are impossible or difficult/expensive to directly measure. The estimated values
(in conjunction with other instrumented measurements) are used as fault diagnosis indices. The edge
device also performs data sampling and filtering and provides outputs for fault detection in the form
of local status indicators and alarms. A Modbus RTU server is implemented on the device, and wider
connectivity to the network core is provided through an additional Serial to Mobile Data (3G/4G)
gateway. The core is able to pull preprocessed and summarized digital data related to local real-time
parameter measurements and estimations at a lower velocity through this gateway, where further
analytics related to PM are applied; this provides an architecture in keeping with the decentralized,
hierarchical structure of the digitalization revolution and IoT [1–3]. The focus of this paper is principally
upon the implementation and testing of the edge device and soft sensors for CM.
Given the relative importance of the ‘100% available plant’ aspirational target, the CM/PM
application in question can be considered to be mission-critical as any ‘false negatives’ indicated by
the system may result in avoidable permanent damage to equipment, and ‘false positives’ may cause
unnecessary system downtime. Both unwanted outcomes may lead to large and unnecessary economic
costs and can potentially interrupt a region’s critical water supply infrastructure. It is therefore of vital
importance to ensure that adequate Verification and Validation (V&V) exercises and prototype field
trials are carried out on any implementation of this edge device. This paper describes such a set of
exercises, carried out on a prototype Commercial Off The Shelf (COTS) microcontroller-based embedded
solution, which is to be deployed locally in a pump house; a potentially harsh industrial environment.
Much previous work has been invested into developing accurate condition monitoring of rotating
equipment in industrial applications; many focus on the use of vibration or acoustic analysis using
real-time data processing techniques such as Wavelet transforms, Machine Learning approaches and
Parameter Optimization (see, e.g., [2,4–8]). However, the specific focus of this paper is on sliding mode
observer techniques, which have also been widely used for condition monitoring and fault diagnosis
in recent years (see e.g., [9–11]). Two of their main advantages are that they exhibit fundamental
robustness against certain kinds of parameter variations and disturbances, while also avoiding the
need for high-speed signal processing techniques required for acoustic or vibration data; a specialized
Sliding Mode Observer (SMO) for fault detection and isolation was developed by Edwards et al. to
exploit these features [10]. Additional key benefits of this observer approach include good accuracy
of operation, combined with comparatively lower-overheads than related techniques [9–11], making
them a good candidate for real-time implementation in an Industry 4.0 context. Therefore, the principal
goal of this paper is to help bridge the theory and practice gap by providing a detailed description
and credible demonstration of an industrial implementation of a simple but effective edge device
implementing this observer-based technique for fault detection of pumping equipment. Both the
observer itself, and the techniques applied to implement the observer in real-time on a small, low-cost
portable embedded system, have a fundamental theoretical grounding. Through laboratory-based
validation using fault injection, combined with online field trials, it is demonstrated that the developed
system seems a good candidate for wider deployment in industry to help achieve the goals of the
second digital revolution.
The remainder of this paper is organized as follows. Section 2 describes the current application
area in more depth, along with the motivation for using observers as soft sensors and their mode
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The remainder of this paper is organized as follows. Section 2 describes the current application
areaThe
in
more
depth,ofalong
with the
motivationasfor
using observers
as soft sensors
and their
mode
of
Sensors
2019,
19, 3781
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remainder
this paper
is organized
follows.
Section 2 describes
the current
application
operation.
describes
themotivation
prototype for
hardware
and software
of mode
the edge
area
in moreSection
depth, 3along
with the
using observers
as softimplementation
sensors and their
of
device,
along
with
a
discussion
of
communication
network
integration.
Section
4
discusses
off-line
operation. Section 3 describes the prototype hardware and software implementation of the edge
of operation. Section 3 describes the prototype hardware and software implementation of the edge
validation
testing
the device, of
while
Section 5 is concerned
with on-lineSection
testing 4(field
trials).off-line
Section
device,
along
with of
a discussion
communication
network integration.
discusses
device, along with a discussion of communication network integration. Section 4 discusses off-line
6 concludes
the paper.
validation
testing
of the device, while Section 5 is concerned with on-line testing (field trials). Section
validation testing of the device, while Section 5 is concerned with on-line testing (field trials). Section 6
6 concludes the paper.
concludes
paper.
2. Slidingthe
Mode
Observers as Soft Sensors
2.
2. Sliding
Sliding Mode
Mode Observers
Observers as
as Soft
Soft Sensors
Sensors
2.1. Sliding Mode Techniques
2.1.
Mode
Techniques
2.1. Sliding
Sliding
Modein
Techniques
As shown
Figure 1, a sliding mode observer is a state observer that employs sliding mode
techniques
to give
accurate
of a system’s
state
in the that
presence
of uncertainty
and
As
in
1, aestimates
sliding mode
observerinternal
is a state
observer
employs
sliding mode
As shown
shown
in Figure
mode
parameter
variations.
The
observer
is
designed
to
maintain
the
sliding
motion,
even
in
the
presence
techniques
techniques to
to give
give accurate
accurate estimates
estimates of
of aa system’s
system’s internal
internal state
state in
in the
the presence
presence of uncertainty
uncertainty and
of faults, and
reconstructs
fault
signals
as a function
of the so-called
equivalent
injection
signal
parameter
variations.
The
is
to
the
motion,
even
the
variations.
The observer
observer
is designed
designed
to maintain
maintain
the sliding
sliding
motion,output
even in
in
the presence
presence
[9–11].
These
estimates
offault
the
unmeasured
fault
signals
be
used
as measures
of equipment
of
faults,
and
signals
as aasfunction
of the
so-called
equivalent
outputoutput
injection
signal
of
faults,
andreconstructs
reconstructs
fault
signals
a function
ofcan
thethen
so-called
equivalent
injection
health,
and
employed
to
raise
alarms
or
to
schedule
maintenance
prior
to
critical
failures
occurring
[9–11].
These estimates
of the unmeasured
fault signals
can
then be
used
as be
measures
equipment
signal [9–11].
These estimates
of the unmeasured
fault
signals
can
then
used asofmeasures
of
in the equipment
being
monitored.
design
procedure
for this
model-based
estimation
approach
health,
and health,
employed
raise alarms
or to
schedule
prior
to critical
failures
occurring
equipment
andto
employed
to The
raise
alarms
or tomaintenance
schedule
maintenance
prior
to critical
failures
is the
principally
characterized
by twomonitored.
components:
(i)design
the design
of anmodel-based
appropriate
sliding surface
where
in
equipment
being monitored.
The designThe
procedure
for this
estimation
approach
occurring
in the
equipment
being
procedure
for this model-based
estimation
the
system
will
demonstrate
the
desired
dynamics
and
(ii)
the
selection
of
an
injection
signal
that
will
is
principally
characterized
by two components:
(i) the design(i)
of the
an appropriate
sliding
surfacesliding
where
approach
is principally
characterized
by two components:
design of an
appropriate
force
the
system
to
reach will
and
maintain
its sliding
motion
[9–11].
the
system
willthe
demonstrate
the
desired dynamics
and
(ii)
the
selection
of the
an injection
that will
surface
where
system
demonstrate
the desired
dynamics
and (ii)
selectionsignal
of an injection
force
system
to reach
and maintain
sliding
motion
signalthe
that
will force
the system
to reachitsand
maintain
its [9–11].
sliding motion [9–11].
Figure 1. Basic operation of a sliding mode observer.
Figure 1. Basic operation of a sliding mode observer.
Figure 1. Basic operation of a sliding mode observer.
2.2. Application
Applicationin
inthe
theWater
WaterIndustry
Industry
2.2.
2.2. Application
in industrial
the
Water Industry
Muchof
ofthe
the
industrial
plantin
inthe
theUK
UKwater
waterindustry
industryconsists
consistsof
of large-scale
large-scalerotating
rotatingmachinery
machinery
Much
plant
suchMuch
ascan
canofbe
be
found
inpumping
pumping
stations.
Forexample,
example,
inconsists
thefirst
firstof
stage
ofaawater
water
treatment
works
such
as
found
in
stations.
in
the
stage
of
treatment
works
the
industrial
plant in
the UK For
water
industry
large-scale
rotating
machinery
a
regulated
supply
of
water
(from
a
reservoir)
is
necessary.
As
precipitation
is
unpredictable,
water
a
regulated
supply
of
water
(from
a
reservoir)
is
necessary.
As
precipitation
is
unpredictable,
water
such as can be found in pumping stations. For example, in the first stage of a water treatment works
can
bedrawn
drawn
fromof
constant
supply
- such as
as
river - that
isprecipitation
close to
toground
ground
levelto
toregulate
regulate
the
be
from
aaconstant
supply—such
river—that
close
level
the
acan
regulated
supply
water
(from
a reservoir)
isaanecessary.
Asis
is unpredictable,
water
reservoir
level,
asshown
shown
inFigure
Figure
In
suchas
anaapplication,
application,
large
amount
ofwater
water
may
haveto
to
be
reservoir
level,
as
in
2.2.In
such
an
of
have
be
can
be drawn
from
a constant
supply
- such
river - that aaislarge
closeamount
to ground
level may
to
regulate
the
pumped
a
considerable
distance
in
order
to
regulate
the
mains
water
supply.
The
equipment
in
such
pumped
a
considerable
distance
in
order
to
regulate
the
mains
water
supply.
The
equipment
in
such
reservoir level, as shown in Figure 2. In such an application, a large amount of water may have to be
stationsisaisrequired
requiredtotohave
have
high
level
and
is relatively
expensive
to repair
if in
damage
stations
a ahigh
level
ofofavailability,
and
is relatively
to equipment
repair
if damage
to
pumped
considerable
distance
in
order
toavailability,
regulate the
mains
water expensive
supply.
The
such
to
couplings
and
shafts
occurs.
The
current
application
is
concerned
with
monitoring
the
health
status
couplings
and
shafts
occurs.
The
current
application
is
concerned
with
monitoring
the
health
status
of
stations is required to have a high level of availability, and is relatively expensive to repair if damage
ofcouplings
the main
shaft
support
bearing
in
this
of pumping
equipment,
to detect
possible
fault
the
main
shaft
support
bearing
in this
kind
of kind
pumping
to detect
possible
fault
conditions
to
and
shafts
occurs.
The
current
application
isequipment,
concerned
with
monitoring
the
health
status
conditions
such
increased
wear,
water
ingress
or
failure. to detect possible fault
such
increased
wear,
water
ingress
coolant
failure. flow
of
theasmain
shaftas
support
bearing
inorthis
kindflow
of coolant
pumping
equipment,
conditions such as increased wear, water ingress or coolant flow failure.
Figure
Figure2.2.Overview
Overviewof
ofcurrent
currentapplication
applicationarea.
area.
Figure 2. Overview of current application area.
The shaft support bearings
are often monitored using spectral analysis of vibration signals [5].
Increased temperature is also a common symptom of bearing wear or of a lubrication failure [11].
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The shaft support bearings are often monitored using spectral analysis of vibration signals [5].
Increased temperature is also a common symptom of bearing wear or of a lubrication failure [11].
several factors
factors such
such as
as ambient
ambient temperature,
temperature, load
load or
or instantaneous
instantaneous rotational shaft speed
However, several
measured bearing
bearing temperature; a simple limit or threshold
threshold
can also cause natural variations in the measured
approach to
detection
of
overheating
is
not
generally
acceptable,
as
it
needs
to
accommodate
a wide
to detection of overheating is not generally acceptable, as it needs to accommodate
a
rangerange
of ambient
and operating
conditions
whilstwhilst
minimizing
the number
of false
alarms.
The
wide
of ambient
and operating
conditions
minimizing
the number
of false
alarms.
robustness
of the
sliding
mode
approach
cancan
take
account
The
robustness
of the
sliding
mode
approach
take
accountofofthese
theseenvironmental
environmental and
and operational
factors, efficiently
efficientlyeliminating
eliminating
them
the detection
fault detection
thus increasing
the
factors,
them
fromfrom
the fault
process,process,
and thusand
increasing
the sensitivity
sensitivity
of the system
diagnosis
systemthe
reducing
the
of false positives.
Compared
withanalysis,
spectral
of
the diagnosis
reducing
number
ofnumber
false positives.
Compared
with spectral
analysis, temperature
using
reliable buttransducers
inexpensive
and
sliding mode
temperature
monitoringmonitoring
using reliable
but inexpensive
andtransducers
sliding mode
techniques
offers
techniques
offers
many including
potential benefits,
including
overall costs,
lower
sampling
rates, and
many
potential
benefits,
lower overall
costs, lower
lower sampling
rates,
and less
computationally
less computationally
intensive
signal [9–11].
processing
techniquesmonitoring
[9–11]. A approach
temperature
intensive
signal processing
techniques
A temperature
alsomonitoring
avoids the
approach
also
avoids
the
problem
of
background
noise
and
vibration
from
other
pumps
interfering
problem of background noise and vibration from other pumps interfering with the data. Additionally,
withlow
thesampling
data. Additionally,
the low sampling
rates
and seem
computational
would
seemand
to
the
rates and computational
intensity
would
to indicateintensity
that low-cost
ADC’s
indicate that low-cost
ADC’s
and microcontroller
may bein
employed
to realize
thethis
CMprovides
system
microcontroller
units may
be employed
to realize theunits
CM system
a simple edge
device;
in amotivation
simple edge
this provides
for their deployment in the current work.
the
fordevice;
their deployment
inthe
themotivation
current work.
2.3. Technical Details
Previous work by Kitsos et al. [11] has shown that a multiple observer
observer approach
approach—with
– with the
observers
monitor coolant
coolant flow
flow and bearing
bearing wear respectively
observers designed
designed as
as virtual
virtual or
or ‘soft’
‘soft’ sensors—to
sensors - to monitor
provides a highly
In general
general terms,
terms, the
the observers
observers are designed to detect early
highly efficient
efficient solution.
solution. In
predictors
either:
(i)(i)
loss
or or
reduction
of coolant
flowflow
to the
e.g.,
predictors of
ofbearing
bearingoverheating
overheatingdue
duetoto
either:
loss
reduction
of coolant
to bearing,
the bearing,
if
theifwater
sumpsump
runs runs
dry or
(ii)orincreased
friction;
e.g., due
wear
and tear,
in oil level,
e.g.,
the water
dry
(ii) increased
friction;
e.g.,todue
to wear
andreduction
tear, reduction
in oil
ingress
of particles
or water
bearing
itself. Aitself.
schematic
and picture
of the bearing
in the current
level, ingress
of particles
or into
water
into bearing
A schematic
and picture
of the bearing
in the
application
is shownisin
Figurein3.Figure 3.
current application
shown
Figure 3. Detail of the shaft coupling.
The observers
are are
basedbased
upon upon
two simplified
heat transfer
of the
arrangement
observersthemselves
themselves
two simplified
heatmodels
transfer
models
of the
shown
in Figure
3. Equation
captures the
dynamics,
while
Equation
(2)
arrangement
shown
in Figure (1)
3. Equation
(1) coolant
capturesheat
the transfer
coolant heat
transfer
dynamics,
while
captures
dynamics:
Equationthe
(2) bearing
capturesheat
the transfer
bearing heat
transfer dynamics:
.
.
= Q FromBearing
− Q CoolantOut.
Q Coolant
= QFromBearing − QCoolantOut
QCoolant
TcoC) c−(Tm.oc −
mC CmCCTCoC=Tohc A=c (ThB c−AT
CcT(iT)o − Ti )
c (oT)B−−m
.
.
= −bT
1 (T
T =Tob (T
) B−−mTbo ) (−Tm−c bT2 ()To − Ti )
.
o
.
QBearing
.
mB CB TB
.
TB
B
1
.
o
c 2
.
o
(1)
(1)
i
.
= QFriction − QCoolantOut − QAtmosphere
.
= µF f N − mc Cc (To − Ti ) − hB AB (TB − Ta )
= a1 N − a2 (To − Ti ) − a3 (TB − Ta )
(2)
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where Ti and To are the coolant inlet/outlet temperatures (◦ C) respectively, TB is the measured bearing
temperature (◦ C), Ta is the ambient temperature (◦ C) and scalars ai and bi are appropriate model
.
coefficients employed to simplify the expressions. The coolant mass flow rate is denoted m (kg/s).
The corresponding sets of equations defining the coolant and bearing observers are given below in
Equations (3) and (4) respectively:
.̂
To
εo
νo
.̂
TB
εB
νB
e. (T̂ − T ) − ν
= b1 (TB − T̂o ) − mb
o
2 o
i
= T̂o − To
= Ko εo
(3)
= e
a1 N − a2 (To − Ti ) − a3 (T̂B − Ta ) + νB
= T̂b − Tb
= KB εB
(4)
where νB and νO are the ‘injection signals’ for the coolant outlet temperature and bearing observers
respectively, and εi are the model errors. Note that an accented variable denotes the observer model
estimation. The observer is designed such that the state error signals are driven toward zero. When the
.
state error is small, any changes in parameters m and a1 caused by loss or reduction in coolant flow or
because of increased heating due to friction can be approximated using Equations (5) and (6) as follows:
.
∆m ≈
νo
b2 (To − Ti )
(5)
νB
(6)
N
where N is the shaft speed (in Hz). The observer has been found to work well under purely simulation
conditions, but to be of practical use in the field an implementation that is capable of functioning
correctly in the relatively harsh environment of a pumping station is required. Remote connectivity to
a control center for data analysis, logging, and predictive maintenance scheduling is also required.
The hardware, software, and communication architectural design of a prototype edge device for this
purpose is described in the next Section.
∆a1 ≈
3. Prototype Edge Device
3.1. Hardware Architecture
Several possibilities were considered for implementing the edge device in hardware, including
the use of Raspberry Pi and Arduino-based devices. However, the microcontroller employed in this
prototype was an Infineon© C167CS housed in a Phycore-167 SBC (Single-Board-Computer) unit.
This unit was mounted on a development board purchased from Phytec©. This configuration was
chosen due to the suitability of the device for deployment in harsh industrial environments (the C167
microcontroller was originally developed for automotive/in-vehicle applications), and the availability
of a reliable/mature development toolchain along with proven static code analysis and execution
time profiling tools. The host CPU features 4 KB of on-chip RAM (IRAM), the CPU and associated
peripherals. The SBC consists of the host CPU, an oscillator/reset/brownout circuit, off-chip 62-KB
FLASH ROM and 2 × 128-KB RAM banks (XRAM), plus a 16-KB non-volatile memory chip (EEPROM)
for storing system configuration information. A 25 MHz CPU clock speed was employed in the design.
Although the C167 has on-chip ADC converters, the resolution was limited to 10-bits. To increase the
resolution to a more acceptable level, an 8-channel 14-bit self-calibrating ADC converter (the AD7856
from Analog Devices Ltd.) was employed. An SPI interface was employed to the host CPU, and 5 V
Zener diodes were employed to prevent the ADC input circuitry being damaged by any excessive
voltages. The hardware employed in the system is shown schematically in Figure 4. Sampling of
input channels occurred at 100 Hz frequency, using a PWM signal generated by the C167 hardware.
employed in the design. Although the C167 has on-chip ADC converters, the resolution was limited
to 10-bits. To increase the resolution to a more acceptable level, an 8-channel 14-bit self-calibrating
ADC converter (the AD7856 from Analog Devices Ltd.) was employed. An SPI interface was
employed to the host CPU, and 5 V Zener diodes were employed to prevent the ADC input circuitry
Sensors
19, 3781 by any excessive voltages. The hardware employed in the system is shown
6 of 16
being2019,
damaged
schematically in Figure 4. Sampling of input channels occurred at 100 Hz frequency, using a PWM
signal
generated
the C167 hardware.
Veryrequired;
little host
CPUsampling
management
wasreduced.
thereforeThe
required;
Very
little
host CPUby
management
was therefore
hence,
jitter was
main
hence,
sampling
jitter
was
reduced.
The
main
intervention
required
by
the
host
CPU
was
the
intervention required by the host CPU was for the extraction of the conversion values once for
every
of the conversion
10extraction
ms, a jitter-insensitive
task.values once every 10 ms, a jitter-insensitive task.
Figure 4. Main hardware elements employed in the system.
Figure 4. Main hardware elements employed in the system.
3.2. Software Architecture
3.2. Software Architecture
Upon power on/reset, the system first performed a software-based self-test, consisting of a ROM
Upon
power
on ‘March’
/ reset, test
the followed
system first
a software-based
of a
checksum
test,
a RAM
by aperformed
timer/oscillator
self-test. The self-test,
latter testconsisting
compares the
ROM
checksum
test,
a
RAM
‘March’
test
followed
by
a
timer/oscillator
self-test.
The
latter
test
amount of CPU cycles elapsing over a fixed duration of the on-chip hardware timers, and compares
compares
the
amount
of
CPU
cycles
elapsing
over
a
fixed
duration
of
the
on-chip
hardware
timers,
these values with the time taken to charge an external resistor/capacitor network with fixed time
and compares
these
with the
time takenato
chargeinitialization
an external resistor/capacitor
with
constant.
If these
testsvalues
are passed
successfully,
system
function is called.network
In addition
fixed
time
constant.
If
these
tests
are
passed
successfully,
a
system
initialization
function
is
called.
to performing system initialization tasks such as setting up the ADC converter, the nine tunableIn
addition tothat
performing
initialization
tasks
such asfrom
setting
up the ADC
converter,
nine
parameters
define thesystem
observer
constants are
extracted
an EEPROM.
Following
this,the
a task
tunable parameters
thatapplication
define the software
observer functionality
constants arewas
extracted
EEPROM.
Following
scheduler
is started. The
brokenfrom
downan
into
six separate
tasks.
this,run-time
a task scheduler
is started. The
The
software architecture
wasapplication
as shown insoftware
Figure 5.functionality was broken down into six
separate
tasks.
The
run-time
architecture
waschannel
as shown
in Figurereadings
5.
The first
task
was
requiredsoftware
to extract
the five ADC
conversion
from the AD7856
via an SPI link to obtain the input signals. The second task was then required to filter the signals using
recursive first-order low pass filters, and convert these readings into appropriate engineering units.
The third task evaluates the observer models, implementing equations (1–4). Based on the filtered
outputs of the observer, the fourth task drives the local indicator status LED’s. To allow the observer to
converge upon power on/reset, the software is required to suppress any faulty/degrading outputs for
a user-adjustable period, typically no more than 10 s. The fifth task updates the main system state
machine once every 50 ms. The sixth and final task handles the Modbus Slave communications via the
hardware UART (Universal Asynchronous Receiver/Transmitter), which handles buffered character
reception and transmission over the 57,600 bps serial link. Static checking was routinely employed to
help enforce best-practice coding techniques during development of the application software, which
also included application of the MISRA rules and related guidelines [12–14].
Sensors 2019, 19, 3781
Sensors 2019, 19, x FOR PEER REVIEW
7 of 16
7 of 16
Figure
5. Main
software
elements
employed
in the
system.
Figure
5. Main
software
elements
employed
in the
system.
The firstthe
task
was required
extract thesystem
five ADC
conversion
readings
from the AD7856
Although
dynamics
of thetomonitored
are channel
relatively
slow moving,
the efficiency
of the
via an is
SPIrelated
link to(inobtain
input
signals.
The second
task
then
filter the signals
observer
part) the
to the
selected
sampling
rate, as
thewas
closer
therequired
observertoapproximates
a
using
recursive
first-order
low
pass
filters,
and
convert
these
readings
into
appropriate
engineering
continuous function the better the performance will be [9]. For this reason, as described above the
units. sampling
The thirdrate
task
evaluates
the observer
models, implementing
equations
Based
the
selected
was
100 Hz, with
cut-off frequencies
for the input digital
filters(1–4).
selected
as 10on
Hz.
filtered
outputs of the observer,
the fourth
drives
the local
indicator
status LED’s.
To allow
The
local communications
were updated
everytask
100 ms.
In order
to meet
the real-time
constraints
of thethe
observer atonon-preemptive
converge upon
power
on / First
reset,
the software
is required
to suppress
any
application,
Earliest
Deadline
(npEDF)
task scheduler
was employed
to control
outputs forThis
a user-adjustable
typically
no moreitthan
10 s. (specifically,
The fifth task
thefaulty/degrading
timing of task executions.
was because ofperiod,
the very
low overheads
requires
updates
the main system
state
machine once
50 ms.
The sixth
and final
task handles
the
Modbus
very
low RAM/ROM
and CPU
overheads,
with every
no shared
resource
handling
protocol
needed),
coupled
Slave communications
via the
hardware
UART
Receiver/Transmitter),
with–amongst
other things-its
optimality
among
the (Universal
non-idling,Asynchronous
non-preemptive
schedulers and its
which handles
buffered
reception
and
transmission
the 57,600
bps serial
link. Static
suitability
for use with
both character
periodic and
sporadic
tasks
[15]. In suchover
a system,
however,
task execution
checking
was
routinely
employed
to
help
enforce
best-practice
coding
techniques
times are required to be comparatively short to prevent undue blocking occurring. Tasks one toduring
five
development
of the application
which
also included
application
of the
MISRA
and
were
created as periodic
tasks, with software,
the required
periods.
As waiting
for the UART
transmit
or rules
receive
relatedtoguidelines
[12–14].
register
be available,
task six (the serial link update task) was created as a sporadic multi-stage
Although
the dynamics
the monitored
system
are relatively
slowthe
moving,
efficiency of the
task [12],
with transmission
andofreception
buffering
employed
along with
16-bytethe
transmit/receive
observer
related
(in part)
to software
the selected
as the ‘C’,
closer
observer approximates
FIFO
on the is
C167
hardware.
The
was sampling
written inrate,
embedded
andthe
compiled/linked
with the a
continuous
function
the
better
the
performance
will
be
[9].
For
this
reason,
as
described
above the
Keil xC16x development toolchain. Timing was verified using the sufficient schedulability condition
selected
sampling
rate was
100 Hz,derived
with cut-off
frequencies
for the input
digital of
filters
selected as 10
based
on CPU
utilization
for npEDF
previously
[16]. Further
descriptions
the verification
local communications
were updated
100 ms.
to meet
the real-time
constraints
of Hz.
the The
functional
and timing properties
of the every
software
for In
theorder
prototype
edge
device have
been
of
the
application,
a
non-preemptive
Earliest
Deadline
First
(npEDF)
task
scheduler
was
employed
documented in an earlier work [17].
to control the timing of task executions. This was because of the very low overheads it requires
3.3.(specifically,
Communications
very Architecture
low RAM/ROM and CPU overheads, with no shared resource handling protocol
needed),
coupled
with
– amongst otherarchitecture
things - its of
optimality
among
the non-idling,
non-preemptive
An overview of the communications
the proposed
system
is shown schematically
in
schedulers
and
its
suitability
for
use
with
both
periodic
and
sporadic
tasks
[15].
In
such
a
system,
Figure 6. As mentioned, the device software and hardware configuration employs a 56,700 bps TIA-232
however,
task
execution times The
are required
to be comparatively
short to prevent
undue
Serial
link for
communications.
device supports
the Modbus Supervisory
Control
andblocking
Data
occurring.(SCADA)
Tasks one
to five were
created as periodic
tasks,
As waiting
Acquisition
protocol,
and implements
a Modbus
RTUwith
slavethe
in required
software.periods.
When valid
Modbusfor
the UART
transmit
or receive
register the
to berequests
available,
task
six (thethe
serial
link update
task) A
was
created
requests
arrive,
a software
task handles
and
prepares
required
responses.
cellular
as
a
sporadic
multi-stage
task
[12],
with
transmission
and
reception
buffering
employed
along
with
Serial/GSM gateway provided (remote) IP connectivity to allow integration into the IoT. The central
the 16-byte
FIFO onRTU
the C167
hardware.
The software
was with
written
in embedded ‘C’,
control
stationtransmit/receive
implements a Modbus
master
implemented
in software,
periodic/sporadic
and compiled/linked with the Keil xC16x development toolchain. Timing was verified using the
sufficient schedulability condition based on CPU utilization for npEDF derived previously [16].
An overview of the communications architecture of the proposed system is shown schematically
in Figure 6. As mentioned, the device software and hardware configuration employs a 56,700 bps TIA232 Serial link for communications. The device supports the Modbus Supervisory Control and Data
Acquisition (SCADA) protocol, and implements a Modbus RTU slave in software. When valid
Modbus requests arrive, a software task handles the requests and prepares the required responses.
Sensors 2019, 19, 3781
8 of 16
A cellular Serial/GSM gateway provided (remote) IP connectivity to allow integration into the IoT.
The central control station implements a Modbus RTU master implemented in software, with
periodic/sporadic
Modbus
transmissions and
retransmissions
to existing
slaves handled
usingscheduling
existing
Modbus transmissions
and retransmissions
to slaves
handled using
master-slave
master-slave
scheduling
techniques
[18].
A virtual
COM
port (vCOM)
driver
provides
a gateway
to
techniques [18].
A virtual
COM port
(vCOM)
driver
provides
a gateway
to carry
the Modbus
RTU
carry
theover
Modbus
RTU
frames
over
Internet
Protocol
and across
Cellulardesign,
network.
In the
frames
Internet
Protocol
and
across
the Cellular
network.
In thethe
prototype
the OnCell
® was employed to provide
prototype
design,
the
OnCell from
G3150A-LTE
Serial
Gatewaytofrom
Moxacellular
G3150A-LTE
Serial
Gateway
Moxa® was
employed
provide
IP connectivity in the
cellular
IP Edge
connectivity
theend-to-end
prototype 256-bit
Edge device,
withenabled.
end-to-end
encryption
enabled.
prototype
device, in
with
encryption
This256-bit
provided
end-to-end
secure
This
provided
end-to-end securetoduplex
serial
communications
to carry
Modbus
traffic
duplex
serial communications
carry the
Modbus
traffic between
the the
network
Core
and between
the Edge
the
network
Coredevelopments,
and the Edge flexible
device. scheduling
In future developments,
flexible scheduling
Core-Edge
device.
In future
of Core-Edge communications
andof
off-loading
of
communications
and
off-loading
of
analytics
workload
between
the
Core
and
Edges
will
be
analytics workload between the Core and Edges will be implemented using state-of-the-art techniques,
implemented
state-of-the-art
techniques, such as those described in [19].
such as thoseusing
described
in [19].
Figure
Figure6.6.Overall
Overallcommunications
communicationsarchitecture
architectureofofthe
theproposed
proposedsystem.
system.
4.4.Offline
OfflineValidation
Validation Testing
Testing
Hardware-In-The-Loop
Hardware-In-The-Loop(HIL)
(HIL)testing
testingas
asaameans
meansfor
forboth
bothearly
earlyand
andlate-stage
late-stagesystem
systemvalidation
validation
in
systems
is now
a well-established
concept
[12,20,21].
The principle
of HIL
inreal-time
real-timeand
andembedded
embedded
systems
is now
a well-established
concept
[12,20,21].
The principle
of
simulation
of an of
embedded
system
is illustrated
in Figure
7; the7;unit
under
test test
(UUT)
executes
on
HIL simulation
an embedded
system
is illustrated
in Figure
the unit
under
(UUT)
executes
representative
hardware,
andand
its outputs
areare
fedfed
directly
to to
the
on representative
hardware,
its outputs
directly
thesimulator.
simulator.The
TheUUT
UUToutputs
outputsare
are
sampled
input
variables
to atohost
dynamic
simulation
model,
which
is evaluated
in realsampledand
andused
usedasas
input
variables
a host
dynamic
simulation
model,
which
is evaluated
in
time.
The simulation
outputs,
which
are are
synthesized
representations
of of
signals
real-time.
The simulation
outputs,
which
synthesized
representations
signalsinternal
internaltotothe
the
dynamic
dynamicmodel,
model,are
arethen
thenfed
fedback
backto
to the
the UUT
UUT thereby
thereby closing
closing the
the control
control loop.
loop. Since
Sincethe
theUUT
UUTisis
representative of the final system implementation, many types of specification defect, omissions or
otherwise unexpected interactions that may result in failures or otherwise unacceptable behaviors may
be removed before the system enters operational service. However, the simulation must be designed
and implemented correctly in order for results to be reliable. If this can be achieved, then HIL testing
can be regarded as a representative virtualization technique which allows a potentially large design
space to be explored and tested, with few (if any) consequences should the UUT malfunction or behave
inappropriately at run-time.
otherwise unexpected interactions that may result in failures or otherwise unacceptable behaviors
representative of the final system implementation, many types of specification defect, omissions or
may
be removed
before
the system
operational
service.
However,
the simulation
must be
otherwise
unexpected
interactions
thatenters
may result
in failures
or otherwise
unacceptable
behaviors
designed
and
implemented
correctly
in
order
for
results
to
be
reliable.
If
this
can
be
achieved,
may be removed before the system enters operational service. However, the simulation must bethen
HIL
testingand
canimplemented
be regardedcorrectly
as a representative
technique
which
allows
a potentially
designed
in order for virtualization
results to be reliable.
If this
can be
achieved,
then
large
design
space
to
be
explored
and
tested,
with
few
(if
any)
consequences
should
the
Sensors
2019,
19,
3781
9UUT
of 16
HIL testing can be regarded as a representative virtualization technique which allows a potentially
malfunction
or
behave
inappropriately
at
run-time.
large design space to be explored and tested, with few (if any) consequences should the UUT
malfunction or behave inappropriately at run-time.
Figure 7. Overview of Hardware-In-The-Loop (HIL) testing of embedded real-time systems.
Figure 7. Overview of Hardware-In-The-Loop (HIL) testing of embedded real-time systems.
Figure 7. Overview of Hardware-In-The-Loop (HIL) testing of embedded real-time systems.
As mentioned, during development of the observer equations, detailed models of the pumping
As
mentioned,
during development
developmentofofthe
theobserver
observerequations,
equations,
detailed
models
of the
pumping
Aswere
mentioned,
models
of the
pumping
process
createdduring
for validation purposes.
The popular
designdetailed
and analysis
package
Simulink©
process
were
created
for
validation
purposes.
The
popular
design
and
analysis
package
Simulink©
process
were created
forimplementation.
validation purposes.
popular
design
analysis
package
Simulink©
was
employed
for their
In The
order
to allow
HILand
testing
of the
edge device
to take
was
employed
for
their
implementation.InInorder
ordertotoallow
allow
HIL
testing
of the
edge
device
to take
place,
was
employed
for
their
implementation.
HIL
testing
of
the
edge
device
to
take
place,
place, the Simulink models of the process were employed along with real-time model simulation
and
the
Simulink
models
the process
process were
wereemployed
employedalong
alongwith
with
real-time
model
simulation
and
the
Simulink
models
of
the
real-time
model
simulation
and
interface devices provided by dSpace©. This allowed three scenario’s (baseline, coolant flow fault
interface
devices
provided
by
dSpace©.
This
allowed
three
scenario’s
(baseline,
coolant
flow
fault
interface devices provided by dSpace©. This allowed three scenario’s (baseline, coolant flow fault
and
bearing friction fault) to be validated on the embedded implementation. A screenshot of the
andbearing
bearingfriction
friction fault) to
implementation.
A screenshot
of the
and
to be
be validated
validatedon
onthe
theembedded
embedded
implementation.
A screenshot
of HIL
the HIL
HIL user interface and testing procedure is as shown in Figure 8. In addition to simulation of the
userinterface
interface and
and testing procedure
8. 8.
InIn
addition
to simulation
of the
bearing
user
procedureisisas
asshown
shownininFigure
Figure
addition
to simulation
of the
bearing
bearing unit itself, type k thermocouples along with signal transmitters and signal isolators of the same
unit
itself,
type
k
thermocouples
along
with
signal
transmitters
and
signal
isolators
of
the
same
unit itself, type k thermocouples along with signal transmitters and signal isolators of the same
characteristics as employed in the field trials (to be described in the following Section) were simulated
characteristicsas
as employed
employed in
inin
thethe
following
Section)
were
simulated
characteristics
inthe
thefield
fieldtrials
trials(to
(tobebedescribed
described
following
Section)
were
simulated
as a part of the overall model.
partof
ofthe
the overall
overall model.
asasaapart
model.
Figure 8.
device
onon
a C167
processor.
Figure
8. HIL
HILtesting
testingofofthe
theprototype
prototypeedge
edge
device
a C167
processor.
Figure 8. HIL testing of the prototype edge device on a C167 processor.
Figure99shows
shows data
data captured
observer
implementation
withwith
the the
Figure
captured comparing
comparingthe
theSimulink-based
Simulink-based
observer
implementation
edge
device
observer
implementation
during
the
baseline
scenario.
Figure
10
shows
data
captured
shows data
captured comparing
observer
with the
edge Figure
device9observer
implementation
during the
the Simulink-based
baseline scenario.
Figureimplementation
10 shows data captured
comparing
the
Simulink-based
observer
implementation
with
the
embedded
observer
edge
device
observer
implementation
during
the
baseline
scenario.
Figure
10
shows
data
captured
comparing the Simulink-based observer implementation with the embedded observer implementation
implementation
the coolant flow
fault scenario.
In this fault scenario,
the coolant
flow rate
is
comparing
the during
Simulink-based
observer
implementation
withcoolant
the
embedded
observer
during
the coolant
flow fault scenario.
In this
fault scenario, the
flow
rate is dropped
dropped
significantly
2150
s
into
the
simulation
run,
and
remains
as
such
for
a
duration
of
600
s.
As
implementation
thesimulation
coolant flow
In such
this fault
the600
coolant
flowberate
is
significantly
2150during
s into the
run,fault
andscenario.
remains as
for a scenario,
duration of
s. As can
seen
dropped
significantly
2150
s
into
the
simulation
run,
and
remains
as
such
for
a
duration
of
600
s.
As
in the captured results, although the coolant flow is not a directly instrumented parameter, the observer
implementation accurately tracks the coolant temperature and estimates the reduced coolant flow
condition. Figure 11 shows data captured comparing the Simulink-based observer implementation
with the embedded observer implementation during the coolant flow fault scenario. In this fault
scenario, the bearing friction factor µ is allowed to drift upwards starting 1000 s into the simulation
run, and increases slowly over time thereafter. As can be seen in the captured results, although the
reduced
coolant
condition.
Figure
11 shows
data captured
the Simulink-based
can
be seen
in theflow
captured
results,
although
the coolant
flow is comparing
not a directly
instrumented
observer implementation
with the embedded
observer
during theand
coolant
flow the
fault
parameter,
the observer implementation
accurately
tracksimplementation
the coolant temperature
estimates
scenario.
In
this
fault
scenario,
the
bearing
friction
factor
μ
is
allowed
to
drift
upwards
starting
1000
reduced coolant flow condition. Figure 11 shows data captured comparing the Simulink-based
s
into
the
simulation
run,
and
increases
slowly
over
time
thereafter.
As
can
be
seen
in
the
captured
observer implementation with the embedded observer implementation during the coolant flow fault
results, In
although
thescenario,
bearingthe
friction
factor
μ is factor
a non–measurable
model
the observer
scenario.
this fault
bearing
friction
μ is allowed to
drift parameter,
upwards starting
1000
Sensors 2019, 19, 3781
10 of 16
implementation
detects
and
starts
to
track
the
gradual
increase
in
the
parameter,
albeit
with
a
small
s into the simulation run,
increases slowly over time thereafter. As can be seen in the captured
offset. although the bearing friction factor μ is a non–measurable model parameter, the observer
results,
bearing
friction factor
µ is
a non–measurable
parameter,
observer
implementation
implementation
detects
and
starts to track themodel
gradual
increase the
in the
parameter,
albeit with detects
a small
and
starts
to
track
the
gradual
increase
in
the
parameter,
albeit
with
a
small
offset.
offset.
Figure 9. Comparative analysis of the Simulink-based and edge device-based observers (baseline
case).
Coolant Mass
Temperature
Coolant Outlet
o
Temperature
Coolant
Flow Rate Coolant
(kg/s) Mass error oC
Temperature
C Outlet
Flow Rate (kg/s)
error oC
Temperature oC
Figure
of of
thethe
Simulink-based
andand
edgeedge
device-based
observers
(baseline
case).
Figure 9.9.Comparative
Comparativeanalysis
analysis
Simulink-based
device-based
observers
(baseline
case).
35
30
35
30
25
Measured
Observer Estimate
25
20
15
0
500
1000
1500
2000
20
15 0
0
-0.02
500
1000
1500
2000
Measured
2500
3000
3500
Observer Estimate
2500
3000
3500
0
-0.04
-0.02
0
500
1000
1500
2000
2500
3000
3500
-0.041.5
01
500
1 0
0.5
0
1000
1500
2000
2500
3000
3500
Observer Estimate
Actual
1.50.5
0
500
1000Observer1500
Estimate 2000
Time (s)
Actual
2500
3000
3500
Figure 10. Comparative
analysis
Simulink-based
and
0
500 of the
1000
1500
2000edge device-based
2500
3000 (coolant
3500flow fault case).
Figure 10. Comparative analysis of the Simulink-based
and edge device-based (coolant flow fault
Time (s)
case).
In all tested scenarios, the results obtained indicated that the observer implementations were
Figure
10. Comparative
analysis
of the Simulink-based
edgeindistinguishable
device-based (coolant
accurate
enough
for the intended
purpose,
giving resultsand
almost
fromflow
the fault
(known)
case).
internal variables in the Simulink model–with less than 5% maximum and 1% average (mean) errors
recorded for the estimated quantities. As such, testing progressed to field trials.
Sensors 2019, 19, 3781
Sensors 2019, 19, x FOR PEER REVIEW
35
Bearing Temperature oC
Temperature error oC
11 of 16
11 of 16
34
Actual
Observer Estimate
33
32
31
30
1000
1500
2000
2500
3000
1000
1500
2000
2500
3000
2000
2500
3000
0.015
0.01
0.005
Friction Factor Estimate
-3
5.5
5
x 10
Observer Estimate
Actual
4.5
4
1000
1500
Time (s)
Figure
Comparative analysis
analysis of
of the
Figure 11.
11. Comparative
the Simulink-based
Simulink-based and
and edge
edge device-based
device-based observers
observers (bearing
(bearing
friction
factor
fault
case).
friction factor fault case).
5. Online Validation Testing
In all tested scenarios, the results obtained indicated that the observer implementations were
Following
off-line
validation
testing,almost
implementation
tests offrom
the edge
device
accurate enoughthe
forsuccessful
the intended
purpose,
giving results
indistinguishable
the (known)
were
then
performed
on
Sulzer©
Water
Pumps
at
Lobwood
pumping
station,
West
Yorkshire,
UK.
internal variables in the Simulink model – with less than 5% maximum and 1% average (mean) errors
As
shownfor
in Figure
12, the quantities.
pump house
threeprogressed
pumps; two
which
are active at any point
recorded
the estimated
Ascontains
such, testing
toof
field
trials.
in time, with the other being ready as standby in case failure. The active pumps are rotated on a
scheduled
basis, allowing
routine checks and maintenance to be carried out on the third. The use of the
5. Online Validation
Testing
observer implementation to detect bearing wear and coolant flow failure will clearly provide guidance
Following the successful off-line validation testing, implementation tests of the edge device were
allowing optimization of the maintenance schedule, helping to prevent both active pump failures and
then performed on Sulzer© Water Pumps at Lobwood pumping station, West Yorkshire, UK. As
unnecessary changeover and maintenance. During online testing, the observer implementation was
shown in Figure 12, the pump house contains three pumps; two of which are active at any point in
deployed on pumps two and three as indicated in Figure 12.
time, with the other being ready as standby in case failure. The active pumps are rotated on a
The Input/Output schedule for the observer implementation was as shown in Figure 13.
scheduled basis, allowing routine checks and maintenance to be carried out on the third. The use of
Temperatures were measured via externally mounted, low-cost type-k thermocouples. ALM-46
the observer implementation to detect bearing wear and coolant flow failure will clearly provide
low-cost head-mount transmitters were employed to convert effective signal ranges to current
guidance allowing optimization of the maintenance schedule, helping to prevent both active pump
transmission, and COM-3B signal isolators were employed to convert to current to voltage prior to
failures and unnecessary changeover and maintenance. During online testing, the observer
conversion by the ADCs. The calibration of the combined sensor, transmitter, isolator and ADC for each
implementation was deployed on pumps two and three as indicated in Figure 12.
temperature measurement channel was as follows: [0 ◦ C:100 ◦ C] → [4 mA:20 mA] → [0 V DC:5 V DC]
→ [0000h :0x3FFFh ]. Stated combined accuracies of the transmitter and isolator (due to linearization)
were ±0.1% and ±0.15% of full-scale max, and the combined accuracy of the ADC converter was
±0.0015% max. Although a full meteorological analysis is deferred to future work, the accuracy of
these measurement chains seems adequate to reproduce results of similar accuracy in the prototype
field trials as in the laboratory-based HIL test results, as models of the same thermocouples, signal
transmitter and signal isolator were employed in the laboratory simulations. During testing, a portable
ultrasonic flow meter was used to measure coolant mass flow rates and hence help to calibrate the
observer. Where appropriate, information on pump speed and power was obtained directly from the
electronic switchgear. The observer device was mounted in an IP67 enclosure with input and output
connections. The observer was installed on-site for long-term trials with data recorded to a standard
Sensors 2019, 19, 3781
12 of 16
desktop via the TIA-232 point-to-point connection, as shown in Figure 14. In total, neglecting wiring
costs,
component costs for the encapsulated observer implementation were ≈ £200.
Sensors 2019, 19, x FOR PEER REVIEW
12 of 16
Figure 12.
12. Arrangement
inside pumping
pumping house,
house, showing
showing pump
pump 22 (left),
(left), pump
(middle),
Figure
Arrangement of
of pumps
pumps inside
pump 33 (middle),
and
pump
1
(right).
Pipework
is
housed
out
of
view
under
the
metallic
walkway.
Sensors 2019, 19, x FOR PEERPipework
REVIEW is housed
13 of 16
The Input/Output schedule for the observer implementation was as shown in Figure 13.
Temperatures were measured via externally mounted, low-cost type-k thermocouples. ALM-46 lowcost head-mount transmitters were employed to convert effective signal ranges to current
transmission, and COM-3B signal isolators were employed to convert to current to voltage prior to
conversion by the ADCs. The calibration of the combined sensor, transmitter, isolator and ADC for
each temperature measurement channel was as follows: [0 °C : 100 °C] → [4 mA : 20 mA] → [0 V DC :
5 V DC] → [0000h : 0x3FFFh]. Stated combined accuracies of the transmitter and isolator (due to
linearization) were ±0.1% and ±0.15% of full-scale max, and the combined accuracy of the ADC
converter was ±0.0015 % max. Although a full meteorological analysis is deferred to future work, the
accuracy of these measurement chains seems adequate to reproduce results of similar accuracy in the
prototype field trials as in the laboratory-based HIL test results, as models of the same thermocouples,
signal transmitter and signal isolator were employed in the laboratory simulations. During testing, a
portable ultrasonic flow meter was used to measure coolant mass flow rates and hence help to
calibrate the observer. Where appropriate, information on pump speed and power was obtained
directly from the electronic switchgear. The observer device was mounted in an IP67 enclosure with
input and output connections. The observer was installed on-site for long-term trials with data
recorded to a standard desktop via the TIA-232 point-to-point connection, as shown in Figure 14. In
total, neglecting wiring costs, component costs for the encapsulated observer implementation were ≈
Figure
£200.
Figure13.
13. I/O
I/OSchedule
Schedulefor
foronline
onlinetesting
testingof
ofthe
thedevice
deviceimplementation.
implementation.
As shown in Figure 15, the online site trial for Pump 3 (fixed speed) took place over a period
of 7 days in which the CM system ran uninterrupted. Tracking of the observer was highly effective,
with the estimated coolant flow remaining constant except during shut down periods (day 2 and
day 4/5), when the flow rate fell to zero following the bearing temperature cooling (as expected).
The bearing friction factor remained constant at the expected level, again indicating fault free operation,
aside from during the shutdown periods when it fell to zero (as expected).
Sensors 2019, 19, 3781
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Figure 13. I/O Schedule for online testing of the device implementation.
Figure 14. Device implementation in IP67 enclosure (bottom right), interface to local PC (bottom left)
instrumentation/communication interfaces
interfaces (top
(top right).
right).
and instrumentation/communication
Figure 16 shows the online site trial for Pump 2 (variable speed) which again took place over
a period of 7 days in which the CM system ran uninterrupted. Tracking of the observer was again
just as effective, with the estimated coolant flow remaining piecewise constant, tracking small speed
changes at several points during the trial. During the course of the experiment, pump trips occurred
eight times; during the longer of these trip events (occurring 8000 s into the trial) the estimated flow
rate fell to zero following the bearing temperature cooling (as expected). The bearing friction factor
remained constant, again indicating fault free operation independent from pump load, aside from
during the longer trip event when it fell considerably (as expected). In summary, both the on-line
and off-line simulations and tests demonstrated that the observer-based edge device functioned as
expected, and that parameter estimations were within acceptable limits for the proposed application.
During the course of the field trials, however, it was noted that some simple improvements to fault
detection and annunciation logic will be required for some specific situations when progressing from
trials to continuous operation; most notably, supressing fault detection and annunciation during
shut-down and trip conditions. These, and other improvements, are currently being implemented,
and a full metrological analysis of the modified system is underway. Overall, however, as the
device is a relatively low-cost solution (as mentioned approximately £200, excluding cabling, gateway,
and network access costs) in comparison with the value of the plant, this gives a very promising
indication of its overall suitability.
Sensors 2019, 19, 3781
Sensors 2019, 19, x FOR PEER REVIEW
C oolant O utlet
Temps oC
30
B earing
Inlet
A m bient
20
Power Signal, V
10
Observer Errors, oC
14 of 16
14 of 16
0
1
2
3
1
2
3
4
5
6
7
1
0.5
0
4
5
6
0.1
0
Coolant Outlet Temperature
Bearing Temperature
-0.1
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
0
1
2
3
4
5
6
7
Coolant Flow
Estimate, l/sec
1.00
0.75
0.50
0.25
0
Bearing Friction
Factor Estimate
x1000
4
2
0
-2
Sensors 2019, 19, x FOR PEER REVIEW
Time (Days)
15 of 16
Temperatures
Figure
Figure15.
15. Online
Online edge
edge device
device implementation
implementation site
sitetrials
trialsresults
resultsdata:
data: Pump
Pump 33 (fixed
(fixedspeed).
speed).
40
x1000
Observer
delta a1
Mass Flow
Estimate, (kg/s )
Errors (oC)
Pump Speed
and Power, V
(oC)
As shown in Figure 15, the online site trial for Pump 3 (fixed speed) took place over a period of
Outlet
7 days in which the20CM system ranCoolant
uninterrupted.
Tracking of the observer was highly effective, with
Bearing
Inlet
Ambient
the estimated coolant flow remaining constant except during shut down periods (day 2 and day 4/5),
0
0
2000
3000the 4000
5000
6000
7000
8000(as 9000
10000 The bearing
when the flow rate fell
to1000
zero following
bearing
temperature
cooling
expected).
friction factor remained
constant at the expected level, again indicating fault free operation, aside
4
from during the shutdown periods Speed
when it fell to zero (as expected).
Pump Trips
2
Pow er
Figure 16 shows the online site trial for Pump 2 (variable speed) which again took place over a
period of 7 days in0which the CM system ran uninterrupted. Tracking of the observer was again just
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
as effective, with the estimated coolant flow remaining piecewise constant, tracking small speed
changes at several0.1points during the trial. During the course of the experiment, pump trips occurred
eight times; during0 the longer of these trip events (occurring 8000 s into the trial) the estimated flow
Coolant
Outlet
rate fell to zero following the bearing
temperature
cooling (as expected). The bearing friction factor
Bearing
-0.1
remained constant,
again
indicating
fault
free
operation
independent
from pump
load, aside from
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
10000
during the longer trip
event when it fell considerably (as expected). In summary, both the on-line and
2
off-line simulations and tests demonstrated that the observer-based edge device functioned as
1
expected, and that parameter estimations were within acceptable limits for the proposed application.
During the course 0of the field trials, however, it was noted that some simple improvements to fault
detection and annunciation
will be
required
some6000
specific
when
progressing
from
0
1000logic
2000
3000
4000 for
5000
7000situations
8000
9000
10000
5
trials to continuous operation; most notably, supressing fault detection and annunciation during
shut-down and trip conditions. These, and other improvements, are currently being implemented,
0
and a full metrological analysis of the modified system is underway. Overall, however, as the device
is a relatively low-cost
solution (as mentioned approximately £200, excluding cabling, gateway, and
-5
0
1000
2000
3000
5000
6000
7000
8000
9000
10000
network access costs)
in comparison
with
the4000
valueTime,
of
the
plant,
this
gives
a very
promising
indication
(s)
of its overall suitability.
Figure 16. Online edge device implementation site trials results data: Pump 2 (variable speed).
6. Conclusions and Further Work
In this paper, developments towards digitization in the water industry have been presented.
Specifically, the development, validation, and field-testing of a real-time edge device as part of a
CM/PM system for deployment upon large-scale pumping equipment in the water industry has been
Sensors 2019, 19, 3781
15 of 16
6. Conclusions and Further Work
In this paper, developments towards digitization in the water industry have been presented.
Specifically, the development, validation, and field-testing of a real-time edge device as part of a
CM/PM system for deployment upon large-scale pumping equipment in the water industry has
been described. Initial field trials with the observer-based edge device have been very promising.
In addition, simulation results and accuracy analysis indicate the system is fit-for-purpose. Future
work will describe longer-term experiences and analyze the fault-detection capability of the proposed
embedded solution. Future work will also describe developments related to integration of multiple
CM edge devices to the network core, and progress the analytics required for optimized operation and
PM of the pumping equipment within a wider Industry 4.0 framework.
Author Contributions: Conceptualization, M.S. and J.T.; methodology, M.S. and J.T.; software, M.S.; validation,
M.S. and J.T.; formal analysis, M.S.; investigation, M.S. and J.T.; data curation, J.T.; writing—original draft
preparation, M.S.; writing—review and editing, M.S.; visualization, M.S. and J.T.; project administration, M.S. and
J.T.; funding acquisition, J.T.
Funding: This research was partly funded by a private industrial research contract with Yorkshire Water PLC.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results
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© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).