Presented at 18th IEEE High Assurance Systems Engineering Keynote speech. Data Quality is important and with Digitization, some fundamental measurement principles are still important

1. Tan Guan Hong
Senior Director, Smart Nation Systems and Solutions
Government Technology Agency of Singapore
High System Reliability
through Design Innovation
18th IEEE High Assurance Systems Engineering

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GovTech’s priorities in Smart Nation
Smart City
Systems
Smart Citizen
Platforms
Digital
Government
Put in place the
technology and
infrastructure
(Smart Nation Platform)
Deliver better and
anticipatory services to
citizens
Empower citizens to
co-create useful
solutions
We are a new ICT and Engineering govt agency formed in Oct 2016

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Objective of the Smart Nation Platform
SNP supports the Government’s vision to transform Singapore into a Smart Nation.
This will be achieved through the following 3 key thrusts:
 Architecting & Implementing Common Infrastructures & Services for WoG’s
use, such as for Communications (through Demand Aggregation), Analytics
Platforms;
 Enabling sharing of physical Discrete Sensors & Cameras deployed by Govt
Agencies, through WoG oversight and WoG sensor deployment (Resource
optimization for potential cost minimization e.g. PUB, SPF’s PolCAM and NEA) ;
and
 Facilitating Sharing of Sensor metadata between Govt Agencies, through
common services such as an API gateway, Video delivery system (potential new
applications – Crowd Density, Littering, Vehicle Counting etc.)

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IoT Vertical Stacks
Sensors
Communication
Data Centre
Visualization & Support
Video Analytics
Data Science
SystemEngineering
CyberSecurity
Define the IoT Eco-System

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Consumer IoTIIoT or Industrie 4.0
Structured Work Process with SOPs
Paid to provide Service
Unstructured Process as dealing with individuals
Pays for Service
Focus on Efficient and Outcomes Conflicting individual goals for self interest & benefit.
Wants & Needs
Highly fragmented, flexible and change over
time fast
Inflexible and Large Organisations,
no single individual ownership
IoT
Influence by stake holders and KPIs Influenced by individuals, social behaviour
and friends
Consumer facingProcess Driven
B2B IoT , Enterprise IIoT or Industrie 4.0
(Reliable Focus)
B2C IoT , Consumer IoT (Cost Focus)

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High System Reliability means:-
• Front End IoT Sensor Devices
• Infra Structure for End 2 End (Power &
Communications)
• Accurate & Reliable Data
• Secure System
• Cost effective and Maintainable
System in Eco-system

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Two-dimensional (2D) camera: These sensors capture data over time frames. Using various video
analytics algorithms, these 2D camera sensors can provide different information. For example, within
the same image, the algorithms can extract information such as (i) people count, (ii) number and
colour of cars (iii) lighting condition, etc. Over time, processed metadata can yield further insights such
as tracking of (iv) people’s movement, (v) dwell time, etc.
Sensor
IoT Sensor Devices:-
Slow Sensor Data: Temperature, Humidity, Hydrostatic pressure, Strain Gauge, Tilt and Infra-red
sensors acquire data in minutes or hours. These are Quasi-static sensors.
Dynamic (Fast) Sensor Data: Accelerometer provides G m/s2 in milliseconds or faster. Acoustic
sound sensor provides voltage signals over time. When these sensor data are processed in the
Frequency Domain using Fast Fourier Transform, the data can provide Peak Vibration Level at various
Frequencies.

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Static
Quasi Static
Dynamic
Periodic
Dynamic
Transient
Sensor
Understand What Parameter you are sensing

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High Repeatability
High Accuracy
High Repeatability
Low Accuracy
Low Repeatability
High Accuracy
Low Repeatability
Low Accuracy
Sensor
7
Which
sensor data
do you trust
& faster to
process in
Real Time ?

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Resolution
Linearity
Sensor
Important parameters when Converting Analog Data into Digital DataVoltage
Voltage DistanceDistance

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Sensor Measurement Error
due to aliasing
Sensor
9

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Understanding Measurement Principle is important !
Actual
Temperature
Sampled
Temperature
Displayed
Temperature
Nyquist
Frequency:-
Sample at
least Twice
the Highest
frequency
Temperature don’t
change at all !
If sample too slow
Temperature is
actually
fluctuating
Sensor

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In sensor system, design with proper
instrumentation grounding rather than have a
poor grounding design and then use filters to
remove the unwanted noise
Prevention is better than cure

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Accuracy of Information depends :-
Accuracy of Sensor
Maintenance & Calibration of Sensor (Function of Time, Drift, Deterioration )
Video Analytics is Processing of Image Data into Structured Information
Accuracy and Repeatability only in controlled environment
Installation of Sensor
Use of Sensor in its context (monitoring & control function)
Expected functional accuracy for decision making
IT’s view is sensor data is stable, repeatable and maintenance free !
While an Electronics view is always drift, accuracy and noise
IT is in Cyber World while Electronics view is deployment into physical
environment which Mother Nature controls)
Sensor

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Accuracy of Sensors
Average Water Depth of 10 m
Water Depth variation of +/- 0.5 m @ 0.1 Hz
in flowing canals
+ 0.5 m
- 0.5 m
Acceptable Accuracy is then +/- 0.25 m
Expected Physical Accuracy to measure
Sensor accuracy needs to be x 2 better to be
cost effective
10 m
Sensor when used outdoor deteriorates over time
Regular Cleaning maintenance, validation and re-calibration
Sensor diaphragm membrane is stiffened by barnacles,
hence affect the readings
Sensor

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Electronic Engineer’s view of Temperature sensors
Temperature Sensors are
Non-Linear Devices:-
Thermistor
RTD
Thermocouple
Temperature
Digitise and
Linearise to T oC
Volt
T oC
IT’s view of Temperature
sensor is Information from
Black Box
Sensor
Engineer’s view of
Temperature sensor
is Voltage & Needs
Digitiser

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Design for Data Quality and NOT just
Availability of Data alone
Sensor
You could also be Sensing unwanted Noise!
SQL
Physical Sensor output can be affected by
Data corruption from
EMI Noise, Humidity, Temperature, Pressure,
Vibration (Lose connections)
Output of data is taken
from a Database and
usually many trust this
data !
When retrieved from SQL dB, the data is Highly
Repeatable and Accurate !
System is Auditable and Computers don’t lie ! 
CyberPhysical

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https://www.isixsigma.com/tools-templates/capability-indices-process-capability/process-capability-cp-cpk-and-process-performance-pp-ppk-what-difference/
When using sensor to measure the physical parameters,
there is a need to understand the Process Capability and
concept of ± 3σ.
When program
interrogate a SQL,
the data feedback
is always at 0 σ !
Everytime
Measurement Capability
Distance
Frequency

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Accelerometer
Sensor on
Railway Track
Digitizer
Electro Magnetic Interference from
Motors, Welding Equipment, etc
Digital DataAnalogue Signals
Use of a Spectrum
Analyzer to check the
Signal to Noise Ratio to
verify Quality of Signal
presented to the Digitizer
Wanted Sensor Signal
EMI Noise
1.0 G = 0.9 G + 0.1 G
= 0.8 G + 0.2 G
Real Data Noise
Sensor
When train passes over the Railway track, it
generates 1.0 KHz vibration levels
What G number are you
actually measuring ?
Signal to Noise Ratio

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Real Impact of Electro-Magnetic Interference (EMI) on
Sensor Information
Sensor
LTA Real Time
Strut Force
Readings
Load(kN)
Lunch Lunch
200 kN
Fluctuating
reduction in
Load = Weight of
15 Merc E200

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Water Sensor readings will change over
time due to Biofouling
Sensor

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Outdoor Data Logging System and Camera uses Solar Panel
as Direct Solar Radiation Shield to reduce Thermal loads
Sensor

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Design for IoT Outdoor Water Quality Sensor
Sensor
Operate
24x7 in
High
Humidity
& Heat

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First Prototype @ Bedok Reservoir tested for 6 months for Reliability
Sensor
System Engineers

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Using Camera as a Sensor
• Accurate & Reliable Data
• Outdoor Operating Conditions are
huge challenges
• One Camera gives many Metadata
and is a Contactless Sensor
Camera as a Sensor

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Camera as a Sensor
High Value Real Time Analytics rather than Forensic
People & Object Detection
Detection of Road
Surface Flood But Not like this
Deployment !
Road Surface Flood
CCTV can be used for:-
Counting Cars, Bicycles
and Humans
Lighting
People Crossing @
Junction
Debris on Roads and
Pavements
Visible Water Pollution
Water level, Water flow

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https://www.technologyreview.com/s/601786/are-face-recognition-systems-accurate-depends-on-your-race/
Facial Recognition needs to
have a trained mugshot
facial data base to get 95%
under controlled conditions
Nice Clear Frontal Mugshots
with many facial points
Clear Multi-view Mugshots with
many facial points
Fewer facial points from one view
Camera as a Sensor

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http://www.dailymail.co.uk/sciencetech/article-3658797/Facial-recognition-ISN-T-reliable-Massive-test-using-million-faces-finds-controversial-
technology-not-accurate-claimed.html
Facial recognition needs pre-trained images to be accurate,
it cannot work without database to compare with !
Camera as a Sensor

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http://www.pbs.org/wgbh/nova/next/tech/the-limits-of-facial-recognition/
The Real Truth about using Video
Analytics to trace the Boston Bombing !
Camera as a Sensor

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Some System Design Considerations
•Sensors selection and location
•Transmitting results from sensor to central data
logger
•Amount of data measurement and data
transmission
•Noise and Surge Protection
•Signal degradation from sensor to signal
conditioner
Design

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System Engineering Approach
Sensors Comms Video Analytics
For a system to work, all 3 sub-blocks must work
Up time++
Each sub-block has 2 states, “0” Not-working Logic
and “1” Working Logic
This system has 2 x 2 x 2 possible combinations (23= 8)
System Engineering

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The system has 3 functional sub-blocks
0 0 0
0 0 1
0 1 1
0 1 0
1 0 0
1 1 0
1 0 1
1 1 1
For system to work, the probability is
1/8 = 12%
The possibility of system not working
is 7/8= 88% !
When getting it to work, can you
assume that the person has the skills
to troubleshoot any of the 7/8 ?
UnlikelyIoT Stack
Sensors
Communication
Data Centre
Visualization & Support
Video Analytics
Data Science
is 26 = 64
1/64=1.5%
System Engineering

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We are trained on how it works
but we are NOT trained to get a
non-working to work…
Troubleshooting demands a
wider range of skills and
innovations
System Engineering

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Start with an
End in Mind
Steven Covey
2. Identify all and Critical
parameters needed at End State
1. Define the End State of Goal
3. Test & Validate Critical
Parameters before Scaling Up
System Engineering

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Full Scale Ops
Deployment
Man Power skillsets
Ops & Tech support
Video bandwidth
Infra
Storage Capacity
Multi-Agency usage
Analytics Accuracies
False Alarms
Improvements of VA
View Angle changes
Proof of Concept
Man Power & Ops
Process
Video Infra
Analytics Accuracies
Start with an End in Mind Steven Covey
All projects to start with a project system
design for operational deployment
Engage stake holders in Ops and project financing
Project Concept formulation must be < 3 months
Proof of Concept must be < 6 months with Mgt Review
Critical Parameter identification and leverage on
external expertise
??
??
If POC is the end ,
then team will only
discover other critical
parameters when
project scales to full
deployment !
Design Thinking Process
System Engineering

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Confidential and Copyright
Hierarchy Tasks Deliverables:
1. Functionality
2. Performance
3. Reliability
4. Convenience
5. Price
R&D / 1 set with Love &Care
Mass Production / 108 sets
Reproducibility
No need for field support
Grandma can use also
Full Scale System

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Confidential and Copyright
Concept to
Prototype
x 1
Lab Model
Prototype to
Pre-Production
x 100
POC
Production to
Sustaining
x 100,000
Production
Functionality
Reliability
Price
Performance
Reliability
Reliability
Price
Convenience
Price
Requirements change from more Nice to Have to
more Need to Have when the Quantities increase
Need 2 Have
Nice 2 Have
Need 2 Have
Full Scale System

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Priority of System
Specification
1.Functionality
2.Performance
3.Reliability
4.Convenience
5.Price
System Engineering

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Commissioning
at functionality
level only
After 6~12 months for
outdoor systems if design
without reliability built-into
the system
Reliability

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Protection & Safety
Schemes to ensure
System Reliability
•Environmental
•Lightning
•Wind Load
Reliability

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System Design for
Outdoor
Environment
Reliability

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IP Protection standard for Data Logger Chassis
IP55
IP67
IP68
Reliability

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Most of the time, many consider only the chassis box and
there are other considerations:-
1. Connectors (material & thickness of plating on pin
connections, sealing method)
2. Heat extraction if modules are enclosed in IP65 casing,
which is good for water proofing. But it also means heat
cannot be extracted out! If the chassis is metal for good
thermal heat transfer, then direct sunlight will also heat
up the chassis. So it is not about buying the IP65 chassis
alone, but rather heat extraction strategy at system level
3. …….other design considerations
Reliability

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Wiring interface with the
IP65 chassis
Type of
connectors
Type of
connection pin
plating and
water proofing
Reliability
Computer
grade
connectors
kept inside
chassis

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Protection & Safety
Schemes to ensure
System Reliability
•Environmental
•Lightning
•Wind Load
Reliability

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NASA website: Data from space-based optical sensors
reveal the uneven distribution of worldwide lightning strikes.
Units: Lightning Flashes / km2/ year.
Reliability

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A Proper Protection System is not just
about buying and installing the correct
part numbers according to catalogue
pictures
Wiring the lightning discharge path
Isolating the signal, power and mains earth
Mains discharge path is different from
signal noise discharge path
Reliability

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Lightning Protection is more than installing
electronics as it needs system design
Data Logger
50,000 volts transient voltage across Piezometer to Data Logger
Reliability

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Lightning Protection
Module Explode
Tree bark peel off due to
lightning discharge
Reliability

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Reliability
Strike on
overhead Lines
Coupling by
Radiation
Rise in Ground
Potential
Affected Equipment
housed inside
building
Electronic
Equipment
damaged NOT
from direct
Lightning Strike !
Lightning
Conductor
50kV 50kV 50kV 50kV250kV
Underground
cables

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Lightning Protection of Instruments and sensors
• Selecting the lightning protection modules
• Installing modules without proper current discharge system
design is NO protection
• Protect at both ends of the cables
• Design discharge ground points for protection modules
• Lightning discharge for Structure Protection is different from
that for Electronic Equipment protection ! Electronic usually can
survive at A while Structures can handle Amps easily
Reliability

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Protection & Safety
Schemes to ensure
System Reliability
•Environmental
•Lightning
•Wind Load
Reliability

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Wind Load of Solar Panels
CP3 / BS6399 Wind Code assumes 35 m/s wind speed
Solar
Panel
Reliability
The horizontal wind force
will create a turning
moments at the base

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Standard outdoor design
• Solar power with battery as backup
• Battery cut off circuit at 10V to protect battery from damage
• Battery box at the base for access and stability
• Lighting protection discharge point at the base
• Solar Panel and Extra Sun Shield used as Heat Buffer
shield for the enclosures and camera casing, to avoid direct
sun light heating up the electronics (“Attap” House)
• Modems and Electronics plastic covers to be removed as
these are heat insulators
• All external wires are protected black flexible PVC shield as
Ultra-violet will harden the rubber insulation
• Battery charging current to be measured when servicingLightning
discharge
Reliability
Turning
moments

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Solar powered systems in Singapore
Reliability

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Path from Innovation
to a Product:
Handling Failures &
Chaos
Innovation

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Many who are
successful,
Can’t handle Failures to
Recover
Failures are
Opportunities to Learn !
Innovation

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58
V+
V-
Vo
G
V- = Vo
( V+ - V- ) G = Vo
If V+ = V- , then Vo = 0
For the OpAmp to be stable, there must be an acceptable
feedback error, which is V+ ≠ V-
So to get feedback, there must be errors made so that
improvements can be made
Good to have Small Error
Feedback
Control System
When you get a cut,
you feel the pain to be
alive !
Innovation

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Old Capability
Limit
Under Challenge - Sure Pass
Over Challenge – Fail !
Time
New
Capability
Limit
Capability
Innovation
Maximum Performance occurs at
the Edge of Failure

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Confidential and Copyright
Product
Drop Test
Concept of using Testing to Failure to Improve Design
Pass
Fail
Fail
Pass
Drop the set at 1 m, Pass Drop Test
Drop the set at 1.2 m, Fail Drop Test
Concept of Test to
Failure to identify
critical component
weakness to
improve
Identify that the bracket needs to have
chamfer to strength stressed corners
for next mold design change
Innovation

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Thank you!