Josifovic, Aleksandar and Corney, Jonathan (2017) Development of

industrial process characterisation through data analysis. In: 2016 IEEE

Symposium Series on Computational Intelligence, SSCI 2016. IEEE,

Piscataway, N.J.. ISBN 978-1-5090-4241-8 ,

http://dx.doi.org/10.1109/SSCI.2016.7849990

This version is available at https://strathprints.strath.ac.uk/61597/

Strathprints is designed to allow users to access the research output of the University of

Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights

for the papers on this site are retained by the individual authors and/or other copyright owners.

Please check the manuscript for details of any other licences that may have been applied. You

may not engage in further distribution of the material for any profitmaking activities or any

commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the

content of this paper for research or private study, educational, or not-for-profit purposes without

prior permission or charge.

Any correspondence concerning this service should be sent to the Strathprints administrator:

strathprints@strath.ac.uk

The Strathprints institutional repository (https://strathprints.strath.ac.uk) is a digital archive of University of Strathclyde research

outputs. It has been developed to disseminate open access research outputs, expose data about those outputs, and enable the

management and persistent access to Strathclyde's intellectual output.

Development of industrial process characterisation

through data analysis

Aleksandar Josifovic

Design, Manufacture and

Engineering Management

University of Strathclyde

75 Montrose street, Glasgow, G1 1XJ

Email: aleksandar.josifovic@strath.ac.uk

Jonathan Corney

Design, Manufacture and

Engineering Management

University of Strathclyde

75 Montrose street, Glasgow, G1 1XJ

Email: jonathan.corney@strath.ac.uk

Abstract—Recently the world has seen an explosion of availabledata, gathered from sensors, experiments and direct measure-ments. The engineering world is shifting from model-centredsimulations to the exploitation of large datasets for insights intomachine behaviour. In the case of optimisation, where usually asimulation model is provided for the exploration of the parameterspace, the new data-driven approach translates into the use ofdata for quantifying problems and delivering an optimal solution.

This paper presents a methodology developed for interpreta-tion of condition monitoring data in the design process and sitecharacterisation of hydraulic fracturing. Field data samples wereprocessed to construct a nominal operating regime in a processpumping system.

After describing the generic process a case study implementa-tion of field data from a North American hydraulic fracturingsite is presented for illustration. The paper summarises sensorinstallation, data acquisition and cloud-based pre-processing.Consequent analysis demonstrates expected operational outputin normal operating conditions for a specified geological terrain.The paper concludes with a base-case scenario for PD pump’sprocess output.

I. INTRODUCTION

In recent years modern engineering systems have witnessed

the start of a shift in the process of product development from

in-depth physical modelling to increasing exploitation of real-

time monitoring of on-site operation. Machine learning, de-

veloped from Artificial Intelligence, aims to provide solutions

for problems based on developed algorithms and analysis of

historical data.

The concept of machine learning has been previously used

in many different industries and scientific studies such as

agriculture [1], mineral extraction [2], optical recognition

[3], robotics [4] and others. In industrial environment, two

approaches prevail; data collection on site and processing in

cloud or, both data collection and processing is done directly

on site.

Hydraulic fracturing process was established in 1950s [5]

and involves using large number of positive displacement (PD)

pumps to pressurise and deliver slurry-water mix into a well

[6]. This method for stimulation and increase of hydrocarbon

recovery has had global economic impact in energy sector, as

demonstrated in North America [7], [8], [9].

In this well stimulation techniques all the central processes

are controlled manually with limited amount of feedback

information related to equipment life and failure prediction.

Data processing of site metrics is currently being recorded

on site and evaluated at central locations. Initial assessment

stage needs to consider present on-site practices to develop

benchmark criteria for future exploration wells. Proposed ini-

tiative considers using characterized process metrics to develop

performance envelops for satisfactory equipment operation.

With the expansion of gathered data potential for predictive

maintenance will be feasible. This paper evaluates mechanisms

for extending equipment’s operational availability by introduc-

ing a methodology for a PD pump monitoring system.

Aim of this work is to establish methodology for develop-

ment of data processing system for hydraulic fracturing.

Systematic approach for implementing machine learning

system involves using previously recorded field metrics to char-

acterise, evaluate amplitudes and assign operational envelopes

with specific phases of the process. Specific milestones in this

work include following:

• Sensor identification and installation on equipment at

process location

• Data acquisition using specialised industrial hardware

• Upload to cloud-based system and access through be-

spoke data software

• Data evaluation using MatLab for identified process

phases

• Establishing performance envelops based on previously

examined data

II. METHODOLOGY

In Figure 1 knowledge discovery diagram is presented.

Source data - For data processing and analysis of hydraulic

fracturing processes a case study was required. Selected data

was recorded in Texas, U.S. in 2015, the upcoming sections

will present additional well properties.

Target data - Pumping stages in hydraulic fracturing typ-

ically last between 60 and 180 minutes. In addition to time

constraint two other determinants that define individual loca-

tion are required flow rates and treating pressures [10]. Case

study identifies a single pumping stage.

Data mining

Data evaluation/interpretation

Source data

Target data

Pre-processed data

Transformed data

Data patterns

Knowledge

Figure 1. Data mining and knowledge discovery diagram

Pre-processed data - Processing site information requires

selection of relevant operational metrics to be recorded for

analysis. In this case, PD pump’s inlet/outlet pressure, speed

and vibration are measured.

Transformed data - Acquired pump metrics are transformed

for display into SI units, pressure in MPa, speed in RPM and

vibration in g.

Data patterns - Physical measurements are aligned and

displayed in the same phases of pumping process. Data char-

acterisation in form of mean, extreme and frequency values

are presented for all sensor measurements.

Knowledge - Cumulative sensor output provides necessary

knowledge for site characterisation in hydraulic fracturing by

defining surface pump pressure and associated vibration for a

given pump speed.

III. CASE STUDY DESCRIPTION

Hydraulic fracturing involves using multiple high pressure

PD pumps to propel fluid downhole. A typical site consists of

multiple frac-truck assemblies each of which carries a single

PD pump.

Photograph of a hydraulic fracturing process site, with

equipment annotation, is shown in Figure 2.

A. Sensor data

One of the pump units was instrumented with a slurry

pressure sensors, a speed sensor, accelerometers on both

power-end and fluid-end together with a number of pressure

and temperature sensors for power-end lubrication, shown in

Figure 3. In addition to PD pump instrumentation, engine

and transmission operation metrics were recorded to monitor

drivetrain performance.

2

1

3

5

4

6

7

1 - PD pump2 - Frac-truck assembly3 - Blender - slurry mixer4 - Water container

5 - Sand container6 - Manifold trailer7 - Wellhead8 - Data collection van

8

Figure 2. Equipment identification on site

PTD

VTF

ST

VTP

PTS

PT - Pressure transducer - discharge

PT - Pressure transducer - suction

VT - Vibration transducer - fluid-end

VT - Vibration transducer - power-end

ST - Speed transducer

D

S

F

P

Figure 3. Sensor allocation on the field unit

All the reciprocating equipment on a frac-site can be related

to the pumping frequency, therefore running the PD pump at

higher speed will demand a higher acquisition rate from the

sensors. For example, a higher acquisition rate is required for

accelerometers to identify spectral energy at higher frequen-

cies.

B. IMS gateway

A key component of the system described is the IMS

gateway, shown in Figure 4. This unit is used to acquire sensor

data, perform signal processing and algorithmic calculations

on the data and utilise its communication interfaces to send

collected information to a cloud server.

Data flow path, illustrated in Figure 5, progresses as follows:

1) Signal conditioning changes transducer output into a

readable value, which is followed by noise attenuation.

For example a 4-20mA value, obtained from the sensor,

is converted into 0-5VDC.

2) The signal conditioned data is then sent to one of the

two data acquisition (DAQ) cards where the analogue

data is sampled and converted into a digital format

Data acquisition module

Electrical Power Supply

20-28Vdc 1.5A

DAQ Inputs Outputs

40 analogue inputs Modbus

0-20mA Ethernet

4-20mA

+/- 10Vdc

Sample rate Frequency

80kHz 8kHz

3 Channels 10 Channels

Single Board Processor Software

Computer i7 Windows 8.1

Communication: Ethernet/J1939/

WiFi/Celular/GPS

Figure 4. Data acquisition unit is installed in the field. Main characteristics are listed in the table.

Field unit Sensor IMS DAQ'Enterprise'

Cloud'SI

Workspace'MATLAB

Figure 5. Methodology for data acquisition from the PD pump unit on site.

that can be read by the Single board computer (SBC).

Accelerometers are recorded at 4 kHz in DAQ card B

and the remaining sensors are recorded at 1 kHz in DAQ

card A.

3) The DAQ data is then sent to the SBC via USB interface.

All the sensor data is stored in a database with time

stamps and associated sensor information (e.g. sensor

type and units of measure)

4) Data is then saved to the solid state drive (SSD). All the

data firstly goes through a buffer and all the data can

be approached almost instantaneously (no need to be pre-

programmed). It can also be automated to acquire specific

data at any available frequency.

5) Data is finally uploaded to the cloud server or physically

transferred by one of the field engineers. The main

challenge with current DAQ system is the volume of data

storage space required to record high speed time series

information. This can quickly run to terabytes of data and

consequently the system is programmed always to erase

the oldest data.

C. Rate of acquisition and data storage

Stage data is stored using three different sampling rates.

Low speed was used to capture the whole stage. Due to the

length of the stage, which could be up to three hours in

length, it was sufficient to have 1Hz sampling frequency to

capture accurately well-head metrics (e.g. sampling frequency

of 1 kHz is used for all pressure sensors, temperature sensors

and rotational encoders installed on the field unit). In contrast,

the accelerometers on the PD pump’s power-end and fluid-end

acquire data at 4 kHz owing to the speed of the reciprocating

drives and the induced equipment vibration on site.

D. Data transfer method

Physical data was downloaded using a specialised software,

‘SiWorkspace’, from the cloud to a local workstation where

data was processed using MatLab. Although SiWorkspace is a

configurable tool it currently only has limited data processing

capabilities and is in principle only used to download the data

from the cloud.

IV. DATA ANALYSIS

The system aims to allow all significant events, seen in the

operation, to be detected and recorded. By evaluating their

magnitude and impact the opportunity arises to predict and

control events or foresee their development. The system would

also be able to send information to the operator and signal

any operational irregularity. For example, excessive vibration

is common on site and to date it’s been difficult to get data to

investigate the cause. In addition, the handlers on site needs

to walk between units during operation and report any leaks

or extreme vibration. There are potential safety benefits which

a control system can provide.

In addition to the speed encoder (measuring PD pump’s

rpm) and discharge pressure sensor, location of which can

be seen in Figure 3, pump is also instrumented with two

accelerometers. In Figure 6 precise orientation and placement

on two main subsections of the pump, power-end and the fluid-

end, can be seen.

To illustrate the system data PD pump pressure and speed

are displayed in Figure 7. It is clear that changes occur

in 44th, 53rd, 94th and 126th minute. All of the changes

can be correlated to variations seen in hydraulic fracturing

stage, thereby, highlighted areas will be analysed further by

using high frequency data. However, there hasn’t been enough

information to justify in-depth discussion of each of these

states as they show great similarities between one another.

Selected data shows operational measurements from two

accelerometers, suction (inlet), discharge (outlet) pressure and

pump speed. Recorded metrics from two accelerometers are

xy

z

PowerEndOrientation

FluidEndOrientation

x

y

z

Fluid End - accelerometerPower End - accelerometer

Figure 6. Orientation of accelerometers on the PD pump

30 40 50 60 70 80 90 100 110 120 130 1400

10

20

30

40

50

60

70

Pre

ssure

(M

Pa)

30 40 50 60 70 80 90 100 110 120 130 1400

20

40

60

80

100

120

140

Time (min)

Speed (

RP

M)

Pump discharge pressure

Pump speed

Formation Breakdown

Increasing speed

Change of gear

Well shutdown

Figure 7. Identifying transitional parts of the stage

Table IDATA FROM TRI-AXIAL ACCELEROMETER LOCATED ON THE PD PUMP’S

POWER END

Sensor Peak (g) Mean (g) RMS (g)

Positive Negativex-accelerometer 5.09 -5.00 -0.03 0.83y-accelerometer 0.39 -0.36 -0.02 0.08z-accelerometer 0.62 -0.69 -0.03 0.11

shown in three coordinate axes (X,Y and Z) at acquisition

frequency of 4 kHz. The remaining sensors are all acquiring

at 1 kHz.

1) Power-End (PE) Tri-axial accelerometer: Time domain

analysis of the crankshaft accelerometer in state one is shown

in Table I.

Analysis in time domain demonstrates significantly higher

energy in x-axis of the crankshaft assembly. Calculating RMS

values shows approximately 10 times more energy in x-axis

compared to y-axis and 7 times more energy than in the z-axis.

50 51 52 53 54 55 56 57 58 59 60−10

0

10

Time (sec)

Acce

lera

tio

n (

m/s

2

50 51 52 53 54 55 56 57 58 59 60−5

0

5x 10

−3

Time (sec)

Ve

locity (

m/s

)

50 51 52 53 54 55 56 57 58 59 60−1

0

1x 10

−6

Time (sec)

Dis

pla

ce

me

nt

(m)

Crankshaft accleration in x−axis

Crankshaft velocity in x−axis

Crankshaft displacement in x−axis

(a)

(b)

(c)

Figure 8. Crankshaft accelerometer and data metrics displaying velocity anddisplacement

Peak values are mostly symmetrical in positive and nega-

tive magnitude. Mean values are relatively low in all three

orientations.

To date the evidence suggests there is only limited inter-

action between the vibration in the three axes, so each of

the axis is considered as an independent function. Because

of the comparatively high energy in one direction further PE

accelerometer analysis and discussion focuses solely on the

x-axis.

Time series data obtained from the PE accelerometer can

be further developed by double integration to evaluate the

crankshaft displacement. Pump’s PE displacement along the

x-axis is quantified in Figure 8 (c). The noisy signal was

processed with 50Hz filter.

Analysis in the frequency domain of accelerometer in x-axis

is displayed in Figure 9.

Accelerometer reading in the x-axis of the PE are shown

in Figure 9. Given that the pump is operating at 106 rpm (i.e.

1.76Hz) it can be seen that the dominant amplitude spikes

are at 455Hz, 15Hz and 135Hz which are listed according to

their power amplitude.

2) Fluid-End (FE) Tri-axial accelerometer: Time domain

analysis of the accelerometer placed on the fluid-end chamber

0 0.1 0.2 0.3 0.4 0.5 0.6−4

−2

0

2

4

Time(sec)

Acceleration (g)

Vibration and Crank angle position

0 0.1 0.2 0.3 0.4 0.5 0.60

60

120

180

240

300

360

Crank Angle (

°)

PEx at 106rpm(1.76Hz)

0 200 400 600 800 1000 1200 1400 1600 1800 20000

200

400

600

800

1000

X: 15Y: 364.2

Frequency plot

Frequency(Hz)

X: 135Y: 303.5

X: 455Y: 806.4

X: 618.3Y: 224.1

Amplitude

X: 910Y: 266.7X: 1038

Y: 196.9X: 1200Y: 157.4

X: 1337Y: 195.7

X: 1500Y: 197.7

PEx at 106rpm(1.76Hz)

Figure 9. Frequency plot of accelerometer placed on the PD pump’s PowerEnd in the direction of the x-axis

Table IIDATA FROM TRI-AXIAL ACCELEROMETER LOCATED ON THE PD PUMP’S

FLUID END

Sensor Peak (g) Mean (g) RMS (g)

Positive Negativex-accelerometer 0.27 -0.28 -0.03 0.04y-accelerometer 4.65 -5.49 -0.03 0.17z-accelerometer 0.28 -0.29 -0.02 0.06

in state one is shown in Table II.

Once again energy in the direction of one of the axes is

visibly higher compared to the other two. Again, displacement

can be evaluated using double integration of the recorded

acceleration. Figure 10 shows fluid-end displacement over the

course of ten seconds.

Because of the nature of the signal it was decided that no

filtering was needed as it will affect the final output. Because

of this it is clear that pulses are equally spaced. This means that

pump running at a constant speed will produce synchronized

pulses that correspond well with plunger movement inside the

pump. This effect is naturally more prominent on the fluid-end

side of the pump because of the closeness to the reciprocating

source (plunger) and the operating medium (slurry).

Analysis of the fluid-end accelerometer in frequency domain

can be seen in Figure 11. Presented data is extracted from the

same time segment as in the analysis of the crankshaft metrics.

From the data displayed in Figure 11 it can be seen that

there are certain similarities to the data presented in the

crankshaft section, which is to be expected. Primarily, the

occurrence of the peak at 455Hz matches earlier results. The

remaining two peaks at 150Hz and 10Hz are close to the

ones exhibited in the PE section. Power amplitudes are lower

in the FE section, vibration peak in time series is lower by

70% compared to the PE.

3) Suction pressure analysis: PD pump’s inlet pressure is a

good indicator of the overall performance and can be used very

efficiently for predicting cavitation [11]. Data is processed and

presented in form of time series and frequency domain.

50 51 52 53 54 55 56 57 58 59 60−50

0

50

Time (sec)

Acceleration (m/s2)

Fluid End accleration in y−axis

50 51 52 53 54 55 56 57 58 59 60−0.01

0

0.01

Time (sec)

Velocity (m/s)

Fluid End velocity in y−axis

50 51 52 53 54 55 56 57 58 59 60−2

0

2x 10

−6

Time (sec)

Displacement (m)

Fluid End displacement in y−axis

Figure 10. Acceleration, velocity and displacement plot from accelerometerlocated on the Fluid End in the direction of the y-axis

0 0.1 0.2 0.3 0.4 0.5 0.6−1.8

−1.2

−0.6

0

0.6

1.2

1.8

Time(sec)

Acceleration (g)

Vibration and Crank angle position

0 0.1 0.2 0.3 0.4 0.5 0.60

60

120

180

240

300

360

Crank Angle (

°)

FEy at 106rpm(1.76Hz)

0 200 400 600 800 1000 1200 1400 1600 1800 20000

50

100

150

X: 10Y: 45.2

Frequency plot

Frequency(Hz)

Amplitude

X: 150Y: 58.35

X: 455Y: 110.5

X: 238.3Y: 45.76

X: 1772Y: 55.12

X: 1820Y: 52.12

X: 1363Y: 36.89

FEy at 106rpm(1.76Hz)

Figure 11. Frequency analysis plot from accelerometer located on the FluidEnd of a PD pump in the direction of the y-axis

Table III shows peak pressure and mean values during one

second.

An entire pumping cycle is displayed in Figure 12 where

pressure is shown in blue and plunger displacement, traveling

from 0 to 360◦, is shown in green on the right axis. Unsurpris-

ingly, the three cylinder pump has three distinct pressure spikes

at 0◦, 120◦ and 240◦ of crank rotation. Pressure fluctuations,

shown in Figure 12, are influenced by opening and closing

of the inlet valves which gives rise to the transient behaviour

inside the inlet manifold of the PD pump. Instrumented PD

pump was operating in the fleet of PD pump units thereby

the possibility of suction pressure fluctuation due to system

interaction must not be ignored.

Peak to peak variation is illustrated in Table III. Positive

Table IIISUCTION PRESSURE OPERATIONAL METRICS

Sensor Peak (MPa)Mean(MPa)

RMS(MPa)

Positive NegativeSuction Pressure 0.53 0.22 0.36 0.36

40 40.1 40.2 40.3 40.4 40.5 40.6 40.7 40.8 40.9 410.25

0.3

0.35

0.4

0.45

0.5

0.55

X: 40.6Y: 0.4045

Inlet Pressure

Pre

ssu

re (

MP

a)

X: 40.54Y: 0.3241

X: 40.28Y: 0.3854

X: 40.33Y: 0.3309

X: 40.1Y: 0.369

X: 40.41Y: 0.4086

X: 40.22Y: 0.4222X: 40.03

Y: 0.4113

40 40.1 40.2 40.3 40.4 40.5 40.6 40.7 40.8 40.9 410

60

120

180

240

300

360

Cra

nk A

ng

le (

°)

Cylinder no.1 Cylinder no.3

Cylinder no.2

Figure 12. Suction pressure on the inlet side of the pump during one pumpingstroke

0 0.1 0.2 0.3 0.4 0.5 0.6−0.2

0

0.2

X: 0.3554Y: −0.02484

Time(sec)

Pressure−mean(Pressure)

Inlet pressure and Crank angle position

X: 0.09983Y: −0.0003123

X: 0.2216Y: 0.06372

0 0.1 0.2 0.3 0.4 0.5 0.60

60

120

180

240

300

360

Crank Angle (

°)

Inlet pressure at 106rpm(1.76Hz)

0 10 20 30 40 50 60 70 80 90 1000

5

10

15

X: 16.67Y: 4.516

Frequency plot

Frequency(Hz)

Amplitude

X: 10Y: 6.576

X: 5Y: 13.27

Inlet frequency at 106rpm(1.76Hz)

Figure 13. Frequency spectrum of the suction pressure on the inlet side ofthe pump during one pumping stroke

pressure peak is approximately 46% higher than the mean

value. Pressure variation in the negative pressure peak is 60%.

It can be seen that peaks accurately align with the crank

angle position and the annotations in Figure 12 indicate precise

timing of the individual cylinders during one pumping cycle.

In Figure 13 a single pumping cycle is analysed in frequency

domain. The regular pressure pattern is visible in the frequency

plot. Results show regular energy spikes at 5Hz, 10Hz and

16.7Hz.

4) Discharge pressure analysis: Time domain analysis of

the discharge pressure has significance due of its influence

on pump performance. Pressure was recorded at the discharge

plenum located inside the PD pump. In the field the possibility

that other PD pumps in the system are influencing operation

of the test unit must also be considered.

Mean pressure during the recording time of two minutes,

along with details on peak values, are shown in Table IV.

Figure 14 displays operation during one pumping cycle. The

slow modulation of the outlet pressure can be the result of the

load and the flow rate. Overall complexity of the signal is

shown in the Figure 14.

Peak to peak oscillation varies in the sampled period.

Section from the later part of the recording, shown in Figure

15, exhibit higher amplitudes.

Analysis in the frequency domain for the discharge pressure

was done on the same time range of one second. Figure 16

Table IVDISCHARGE PRESSURE

Sensor Peak (MPa)Mean(MPa)

RMS(MPa)

Positive NegativeDischarge Pressure 43.80 70.18 57.18 57.34

80 80.1 80.2 80.3 80.4 80.5 80.6 80.7 80.8 80.9 8145

50

55

60

65

70

75

X: 80.26Y: 54.11

Outlet Pressure

Pre

ssure

(M

Pa)

Time (s)

X: 80.29Y: 66.53

80 80.1 80.2 80.3 80.4 80.5 80.6 80.7 80.8 80.9 810

60

120

180

240

300

360

Figure 14. Discharge pressure inside outlet plenum of the pump during onepumping stroke

displays results from this analysis.

Frequency spectrum, shown in Figure 16, depicts impulsive

nature in the low frequency part of the pressure signal. It is

clear that there is a strong frequency concentration at 18Hz

forming 10:1 ratio with plunger frequency of 1.76Hz.

V. RESULTS

Synchronization in the time domain and alignment against

crankshaft position provides useful insight towards understand-

ing pump operation in the field environment. Clear correlations

between different sensor measurement can be seen and some

of their relationship have been presented in this paper. In

Figure 17 these observations are highlighted.

For example the accelerometer on the fluid-end, in the

direction of the y-axis, shows equally spaced pulses that

110 110.1 110.2 110.3 110.4 110.5 110.6 110.7 110.8 110.9 11145

50

55

60

65

70

75

X: 110.5Y: 51.82

Outlet Pressure

Pre

ssure

(M

Pa)

Time (s)

X: 110.5Y: 68.66

X: 110.7Y: 52.2

X: 110.7Y: 69.31

110 110.1 110.2 110.3 110.4 110.5 110.6 110.7 110.8 110.9 1110

60

120

180

240

300

360

Figure 15. High amplitude variation of the discharge pressure inside the outletplenum of the PD pump during one pumping stroke

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5−10

−5

0

5

10

X: 0.1327Y: 6.934

Time(sec)

Pressure−mean(Pressure)

Outlet pressure and Crank angle position

X: 0.3273Y: −7.344

0 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.50

60

120

180

240

300

360

Crank Angle (

°)

Outlet pressure at 106rpm(1.76Hz)

0 10 20 30 40 50 60 70 80 90 1000

500

1000

1500

2000

2500

X: 18Y: 2310

Frequency plot

Frequency(Hz)

Amplitude

X: 12Y: 642.1

Outlet frequency at 106rpm(1.76Hz)

Figure 16. Frequency spectrum of the discharge pressure on the outlet sideof the PD pump during one pumping stroke

0 30 60 90 120 150 180 210 240 270 300 330 360-1

-0.5

0

0.5

1FluidEnd acceleration

Crank Angle (°)

Y-direction

0 30 60 90 120 150 180 210 240 270 300 330 360

0.35

0.4

0.45

0.5Inlet Pressure

Pre

ssu

re (

MP

a)

0 30 60 90 120 150 180 210 240 270 300 330 36055

60

65

70Outlet Pressure

Pre

ssu

re (

MP

a)

Crank Angle (°)

Crank Angle (°)

Acce

lera

tio

n (

g)

Figure 17. Important pump metric and their relationship during one fullpumping cycle

correlate well with angle of the crankshaft. Similarly, inlet

and outlet pressure show strong association with the type of a

PD pump (i.e. triplex) in the output signal. Pressure variation

is also presented and +/- 10% fluctuation is seen around the

mean value.

VI. CONCLUSION

In conclusion, the information provided in this paper illus-

trates the complexity of data being collected from industrial

operation to monitor site behaviour. This analysis highlights

the field operation and the challenges that have to be overcome

before the goal of a comprehensive mechatronic site control

system can be realized.

The analysis of the accelerometers has shown that the

vibration along the length of the pump is dominate. This

presents valuable information that needs to be further devel-

oped to support structural analysis. For example, similar field

data analysis has previously been reported for wind turbines

where the fatigue cycle was measured and implemented in

computational model to assess possible improvements and

design modification [12], [13].

In pressure pulsation studies, scenarios showing steady state

and transitional operation were displayed and discussed which

have possibility to be used as system training data sets.

Future implementation of this approach in industrial systems

could provide live monitoring and feedback to equipment op-

erator. Lastly, the condition monitoring system could provide

information about any outstanding trends or predict failure

based on this method for developing training data sets.

REFERENCES

[1] R. J. McQueen, S. R. Garner, C. G. Nevill-Manning, and

I. H. Witten, “Applying machine learning to agricultural

data,” Computers and electronics in agriculture, vol. 12,

no. 4, pp. 275–293, 1995, URL.

[2] J. Tessier, C. Duchesne, and G. Bartolacci, “A machine

vision approach to on-line estimation of run-of-mine ore

composition on conveyor belts,” Minerals Engineering,

vol. 20, no. 12, pp. 1129–1144, 2007, URL.

[3] A. Fathi and G. Mori, “Action recognition by learning

mid-level motion features,” in Computer Vision and Pat-

tern Recognition, 2008. CVPR 2008. IEEE Conference

on. IEEE, 2008, pp. 1–8, URL.

[4] J. R. Peters, “Machine learning of motor skills for

robotics,” Ph.D. dissertation, University of Southern Cal-

ifornia, 2007, URL.

[5] G. E. King and Apache, “Hydraulic fracturing 101: What

every representative environmentalist regulator reporter

investor university researcher neighbor and engineer

should know about estimating frac risk and improving

frac performance in unconventional gas and oil wells,”

in SPE Hydraulic Fracturing Technology Conference.

Society of Petroleum Engineers, 2012, URL.

[6] Daneshy Consultants International, “Hydraulic fracturing

to improve production,” Tech101, 2010, URL.

[7] Rohan North, “Hydraulic fracturing market (online),”

Accessed: 16 April 2016, 2016, URL.

[8] API, “Hydraulic fracturing unlocking americas natural

gas resources,” Accessed: 16 April 2016, 2014, URL.

[9] Oil and Gas - Financial journal, “Hydraulic fracturing

market projected to continue growth,” Accessed: 16 April

2016, 2015, URL.

[10] M. J. Economides, K. G. Nolte, U. Ahmed, and

D. Schlumberger, Reservoir stimulation. Wiley Chich-

ester, 2000, vol. 18, ISBN: 978-0471491927.

[11] P. J. Singh and S. D. Able, “Determination of npshr

for reciprocating positive displacement pumps-a new ap-

proach,” in PROCEEDINGS OF THE INTERNATIONAL

PUMP USERS SYMPOSIUM. TEXAS A&M UNIVER-

SITY SYSTEM, 1996, pp. 131–142, URL.

[12] B. Jouan, S. Bergholz, and J. Rudolph, “Fatigue mon-

itoring approaches for power plants,” in ASME 2014

Pressure Vessels and Piping Conference. American Soci-

ety of Mechanical Engineers, 2014, pp. V001T01A035–

V001T01A035, URL.

[13] S. Bergholz, J. Rudolph, and A. Willuweit, “Famos 4

wind a new method for the fatigue monitoring of wind

energy plants,” Procedia Engineering, vol. 133, pp. 84–

89, 2015, URL.