Capacity for Rail

Monitoring Technologies & Sensors identificationMigration of innovative technologies to existing structures

Madrid – 21/09/2017

Mani EntezamiResearch Fellow

Overview

2

• Sensing technologies and energy harvesting

• Technology identification and evaluation

• Sensors assessment

• System design

• Field tests

• Demonstrator

• Conclusions

Identified Technologies

Technology to be Developed

Technology Evaluation

Context

Capacity for Rail

Sensing Technologies

Sensing technologies

4

Technology Key Factors

Vision Frame rate, Resolution, Colour depth / sensitivity, Field of vision (lens angle)

VibrationRange of operation, Sensitivity, Drift / stability, Sampling rate, Power consumption,

Shock tolerance

Sensor resonance frequency

Mechanical

(Strain)

Bridge configuration, Robustness (operating environment), Target geometry,

Installation method

Environmental

(Temperature, Humidity,

Wind)

Range of operation, Robustness, Accuracy vs. cost, Size, Installation method

AcousticRange of operation (sensitivity), Frequency range, Directionality, Physical limitations

(size)

Robustness / operating principle

ElectricalRange of operation, Sensitivity, AC or DC, Sampling rate, Physical limitations (size)

Installation method, Isolation

Specialist / Multifunction

(Fibre)

Operational benefits arising from multi-modal operation (sensing and

communications)

Sensing technologies

5

Vision

Displacement of the drive rod

Camera results vs displacement sensor

Vision

High speed camera - FASTCAMMovement of the overhead lines

Eurostar @ 300 km/h

Energy harvesting and storage

6

Solar panelThermogeneratoer• Energy Harvesting• Solar • Thermal• Wind• Electromagnetic• Vibration

• Storage• Batteries • Flywheels • Supercapacitors• Hydrogen • Thermal

Wind TurbineGel battery

Supercapacitor

Example: Solar panel power depends on the location

Monthly average generated power

Capacity for Rail

Technology identification and evaluation

Technology identification framework

8

Drivers

Technology Market Place

Technology

Capability

Barriers Applicability

Enabler

Required Competency

Environmental

Technological

Social

Economic

Political

Which technologies could be developed given the Market Place?What is missing in the Market Place to progress the technologies?

What is the technology in question?

Technology identification framework

9

Drivers

Technology Market Place

Which technologies could be developed given the Market Place?What is missing in the Market Place to progress the technologies?

Technology

Capability

Barriers Applicability

What is the technology in question?

Where could the Technology be applied?

Where are the drivers for introducing the Technology?

Where are the barriers to introducing the Technology?

What capability is required to realise the Technology?

Technology identification framework

10

Applicabilityapplication, stakeholders, business case and business model

Wireless Vibration Monitoring

Driverspolitical, economic, social, technological, environmental

Barrierspolitical, economic, social, technological, environmental

Capabilityenablers, competencies and relevant stakeholders

No cablesNo trip hazardEasy to install and retrofitTrack movements

Accelerometers and gyroscopes

Signal processing

Communication

Power

Track access and possession

Power consumption

Energy harvesting environmental limitations

Switches and crossings

Track geometry monitoring

Wheelflat detection

• Testing has been undertaken at the Long Marston facility, Uk

• A variety of different grade (cost) accelerometers have been evaluated

• Cross-comparison of sensors and evaluation against geophones

• High quality (cost) sensor displays reasonable correlation

• Lower quality (cost) sensor displays significant drift – can be reduce by post processing

11

Sensor evaluation / comparison

7 7.2 7.4 7.6 7.8 8-0.15

-0.1

-0.05

0

0.05

0.1

0.15

0.2

0.25

Time into experiment [s]

Vert

ical speed [m

s-1]

geo2

vz2

Accelerometers

12

KS76a (Piezo) ADXL001 (MEMS)

Interface IEPE Voltage

Power ~ 132 mW < 1 mW

Range ±120 g ± 250 g

Resonant frequency > 34 kHz 22 kHz

Sensitivity 50 mV/g 4.4 mV/g

Noise 80 µg (20 – 50000 Hz) 95 mg (100 – 400 Hz)

• MEMS vs Piezo• MEMS average draw of

0.75 mW compared to Piezo of 132 mW

• MEMS Peak draw of 5 mA (1.5 mW)

Wide temp range (-40 to 85 ˚C)

Sensor Evaluation / Comparison

13

Vibration calibrator

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-2

-1

0

1

2MMF Sensor

Accele

ration [g]

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2-2

-1

0

1

2Analog Device Sensor

Time [s]

Accele

ration [g]

Piezo

MEMS

• Lower SNR in MEMS• Good match in Freq-

domain and in displacement calculation

0 100 200 300 400 5000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Frequency [Hz]

Am

plit

ude(f

)

100 150 200 250 300 350 400 450 5000

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Frequency [Hz]

Am

plit

ude(f

)159.2 Hz calibration signal

Piezo MEMS

• Example of sensor evaluation using the framework

SENSORS

Ref Description Au

tom

ate

d d

ata

co

lle

cti

on

De

tec

tio

n o

f in

cip

ien

t fa

ult

s

Ev

en

t lo

ca

liza

tio

n

Wa

ke

up

un

de

r e

ve

nt

Dif

fere

nt

tim

e f

or

sen

sin

g/s

en

din

g

Sc

ala

bil

ity

En

vir

on

me

nta

l c

om

pa

tib

liit

y

Da

ta c

oll

ec

tio

n a

t li

ne

sp

ee

d

Dif

fere

nt

me

asu

rem

en

t m

od

es

Cu

sto

m r

ep

ort

ing

of

pa

ram

ete

rs

Cu

sto

m f

au

lt d

ete

cti

on

ru

les

Cu

sto

m s

ub

mis

ion

ra

te o

f m

ea

sure

me

nts

Se

lf-d

iag

no

stic

Lo

ng

te

rm s

tab

ilit

y

Lo

ng

te

rm r

ob

ust

ne

ss a

nd

re

lia

bil

ity

Ca

lib

rati

on

Ge

om

etr

ica

l c

om

pa

tib

ilit

y

Co

mp

ati

bil

ity

wit

h t

rac

k m

ain

ten

an

ce

Hig

h a

va

ila

bil

ity

on

co

mp

on

en

t le

ve

l

Hig

h a

va

ila

bil

ity

on

se

nso

r n

od

e

Re

sist

an

ce

to

ele

ctr

om

ag

ne

tic

fie

lds

Mo

un

tin

g s

imp

lic

ity

SE

NS

OR

SC

OR

E

WEIGHT 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5% 5%

S1 MEMS Accelerometer ADXL345 10 10 10 10 10 5 10 10 0 0 10 10 0 10 10 5 10 10 10 10 10 5 8.0

Capacity for Rail

System Design

System Design

15

Vibration based track movement motoring system using accelerometer on sleepers

➢ Low power ➢ Low cost➢ Easy installation

➢ No wires➢ Use of an energy harvesting system➢ Remote access

System design

16

Wireless Vibration Nodes

Wayside central unitWith remote connection

Short range and low power wireless technology

Use of EH for track side system

UoB wireless node system overview

17

Accelerometer

MCU + Memory

ISM Wireless Module

Temperature

Battery Voltage

ISM Wireless Module

ARM device

SD Memory

Card

WiFi / 3G module

Low-powerLow-frequencyISM band

Sleeper Node

Master Node

System components

18

➢ Data acquisition and processing• Microchip PIC24• Low cost, low power• Easy to integrate with the sensor (SPI bus)• Capability for communication modules• Suitable processing power and • Data storage• Wide temperature range (-45 to 85 ˚C)

➢ Short range wireless system• Microchip MRF89XAM8A• Low frequency ISM band, 868 MHz• Ultra low-power• SPI Interface with Interrupts• Small size: 18 mm x 28 mm• Wide temperature range (-45 to 85 ˚C)

Cost about £3

Cost about £5

COMMUNICATIONS

Ref Description Fast

dat

a tr

ansm

issi

on

Wir

ele

ss c

om

mu

nic

atio

n

Stan

dar

d i

nte

rfac

e f

or

wir

ele

ss

Ind

ust

rial

Eth

ern

et

com

mu

nic

atio

n

Cu

sto

m m

ess

age

fo

rmat

Tim

e s

yncr

on

izat

ion

CO

MM

UN

ICA

TIO

N S

CO

RE

17% 17% 17% 17% 17% 17%

C1 MRF89XAM8A 5 10 10 0 10 10 7.5

Wireless module evaluation

Central unit and remote connection

19

• Low power and low cost• Use of energy harvesting• Remote connection• Storage system• Weatherproof• Easy integration into final product: minimise product

development, quicker time to market• Capable of using the selected wireless technology

• Raspberry PI 3A➢ Quad core ARM8 processor➢ Serial communication bus➢ Storage – SD card➢ Network connections➢ Linux OS - Open source➢ Small and easy to mount➢ Low power (<3W)

• GPS module – Time sync• Short range wireless • IP67 Enclosure• Operation range

– (-40 to 85 ˚C) without LAN– (0 to 60 ˚C) with LAN, used only for

remote access

£26

£5

£10

All together with enclosure ~ £50

Energy systems

20

ENERGY HARVESTING

Ref Description Suit

abil

ity

for

inst

alla

tio

n a

t d

iffe

ren

t si

tes

Mo

nit

ori

ng

and

re

po

rtin

g o

f b

atte

ry s

tatu

s

Self

-dia

gno

stic

Envi

ron

me

nta

l co

mp

atib

lity

Re

sist

ance

to

ele

ctro

mag

ne

tic

fie

lds

Mo

un

tin

g si

mp

lici

ty

ENER

GY

HA

RV

ESTI

NG

SC

OR

E

Weight 17% 17% 17% 17% 17% 17%

E1 Solare panel BP SX20U 5 5 0 5 10 10 5.8

• 50 cm automotive solar panel (used in traffic lights)

• Up to 20 W power• Easy to install on the track-

side

• Wide operating temperature range

• Resilient unit, does not require further housing / protection

UoB sleeper node

21

• Easily deployable networks of sensors

• Internal accelerometer• ‘Sleeps’ until a train is detected• Samples at 1600 Ss-1

• Downsamples to 800 Ss-1

• Stored in local memory• Transmitted to master node

after train has passed• Battery powered

• ~5 years (3A Lithium Iron) • EH for local master node

• Includes a temperature sensor • Wide temperature range to

operate (-20 to 60 ˚C)Around £20 for a prototype

Capacity for Rail

Field Testing / Demonstration Activities

UoB - Live trial initial tests

23

• Monitoring sleepers on the UK High Speed 1 line using low power accelerometers and embedded microcontrollers – Eurostars, 300 km/h

– Javelins, 220 km/h

– Freight, 100 km/h

• Around 1400 train passages were recorded over a 2 week period

Data analysis - Accelerometers

24

• Displacement curves for the three accelerometers

• One is significantly larger than the other two

Less-well supported sleeper

Sensor evaluation

25

10 kS/s

0.8 kS/s

UoB & UPorto - Live trial demonstration testing

26

• Transition zone onto a bridge

• Use of EH at trackside

• Battery powered nodes

• Short range wireless system

• LTE for remote access

Alcácer do Sal

27

4x Battery power Wireless nodes

Vibration node with wireless power, Solar and battery (UPORTO)

Energy harvesting~120W

Central Units

Alcácer do Sal

28

Main control panel

4G Router

UPorto PU

Gel Batteries and Regulating system

UPorto Vibration Node UoB Vibration Node

UoB Central PU

29

Alcácer do Sal

UoB - Recording on April 20 at 10:54am

Results

30

• Vibration data in m/s2

• Different vibration behaviour

• Different vibration level

• Interesting peaks on

Node 2

Data available live on UoB webserver

• Displacement in mm

• Using a bandpass filter and double integration for displacement

• Consistency in number of wheels passed

• Consistency in the displacement level

Data Analysis

31

• Freight train – Peak analysis

• Crest factor spectrom

• Load monitoring and wheel impact

System performance

32

• Waking up every 200ms

• Log maximum 11 seconds if there is a train

• Estimated to work for two months at 25˚C

• Replaced battery after 40 days

• Difference between industrial and commercial batteries

• Triggering to be improved

System performance

33

• Temperature effect on the batteries of the nodes

• No significant change in battery voltage within a month

• Temperature monitoring

• Max temp about 60˚C– Lost network connection

• Data stored locally

• Connection retrieved after a couple of weeks

Instrumentation plan – NR HS, UK

34

➢ 16 wireless nodes➢ 14 battery powered➢ 2 solar panels and battery

➢ Vibration and temperature monitoring➢ Lateral acceleration - 4 nodes➢ Vertical acceleration – 12 nodes

35

Network Rail High Speed - Kent

• Location identified by NR HS• Ballast issues• Nodes installed • Solar panel • Access for UoB staff visit to

be granted

Picture provided by NR inspection Helicopter

Wireless nodes

Solar panel

DB Demonstrator

36

• Mesh network• ~250 sensors – battery

powered• Three gateways • Solar panel for the gateways• Sensor grid within the rail

expansion • Tilt-sensors on the cess and

four-foot sides of the sleepers

Conclusions

37

• Sensing technologies introduced including their key parameters for evaluation

• Field test demonstrations of a range of technologies– Extends laboratory testing– Demonstrates integration of technologies

• Monitoring system design comprising of:– Low cost system– Energy harvesting – Low power electronics and sensing technologies– Short range wireless methods and LTE networks– Distributed data collection and processing network– Data processing and condition monitoring techniques

• Demonstrates interactions / trade-offs between technologies in whole system development

Thank you for your kind attention

Mani Entezami Research Fellow

The University of Birmingham

m.entezami@bham.ac.uk