IoT Analytics: there's not just predictive maintenance. The global C4IR(TM) event series is available to franchise via www.cir-strategy.com as C4IRx.

1. IoT analytics: There’s not
just predictive maintenance
Dr. Boris Adryan
Head of IoT & Data Analytics
Zühlke Engineering GmbH
@BorisAdryan
Presented at Consortium for the 4th Revolution Executive Briefing Day (C4IR-1
Cambridge, UK 2-3 February 2017 www.cir-strategy.com/events

2. Zühlke: Empowering Ideas
Business Innovation - from idea to market success
founded in 1968
> 8.000 projects
800 employees
120 million EUR turnover (2015)
key verticals:
manufacturing, systems engineering
medical & pharma
financial sector
consumer products
The Internet of Things
is a key ingredient to merge the digital
and the real world to provide novel
business opportunities.
Your partner for business innovation
Zühlke Engineering unites business &
technological competence: digital
solutions for a connected world.

3. The Internet of Things

4. Yes, but why?
IoT
M2M
asset
tracking
remote
access
information
systems new business
models
supply & demand
maintenance
pay-per-use third-party
apps
firmware
updates
customer
support
predictive
maintenance
condition
monitoring
supply chain
management

5. Only today!
Live and exclusive at C4IR:
Mostly NDA stuff.

6. Predictive maintenance
Case study: Drill bit of a milling machine
Image source:
Wikipedia
• industrial drilling is highly automated
(CNC)
• the drill bit is an expensive
consumable
• changing the drill bit too late can
• impinge on product quality
• destroy the product
• destroy the machine

7. often: condition-based replacement
Maintenance strategy
not considering remaining useful lifetime
often, the “condition” can only be guessed
best approximation: time in use
based on statistical considerations
(still a guess, but it’s educated!)
predictive!

8. Predictive maintenance
Remaining useful lifetime
“some
quantitative
measure”
bad
good
time
“critical”
change
here!
not
here
definitely
not here! f(t)

9. Predictive maintenance
Remaining useful lifetime
time
g(t)
h(t)
i(t)
f(t) = c1 g(t) + c2 h(t) + c3 i(t) + …
hard to
measure
easier to
measure

10. Predictive maintenance
Remaining useful lifetime
param 1
param 2
param 3
param 4
param 5
param 6
target
condition-based
‘safe point’
critical
threshold
RUL, param 1-6
dependent
t
obtain training data in
experimental setup
our f(t)
our g(t), h(t), i(t) + …

11. data recording model building test use in production
data recording
(production system)
evaluation
raw data clean-up
feature
engineering
model
learning
model
selection
labour intense compute intensebrain intense
Machine learning pipeline
development
production

12. The Internet of Things
data storage+
compute

13. distributed local experimental
pipeline complex simple simple
model building hit-or-miss hit-or-miss simple
model update complex simple simple
production system “lab”
Learning on development vs
production system
data
resources
proddev

14. Edge, fog and cloud computing
Edge
Pro:
- immediate compression from raw
data to actionable information
- cuts down traffic
- fast response
Con:
- loses potentially valuable raw data
- developing analytics on embedded
systems requires specialists
- compute costs valuable battery life
Cloud
Pro:
- compute power
- scalability
- familiarity for developers
- integration centre across
all data sources
- cheapest ‘real-time’
option
Con:
- traffic
Fog
Pro:
- same as Edge
- closer to ‘normal’ development work
- gateways often mains-powered
Con:
- loses potentially valuable raw data

15. The same principles apply,
even if it’s not strictly IoT

16. Analytical response times for IoT
microseconds
to seconds
seconds to
minutes
minutes
to hours
hours to
weeks
on
device
on
stream
in batch
am I falling?
counteract
battery level
should I land?
how many
times did I
stall?
what’s the best
weather for
flying?
in process
in database
operational insight
performance insight
strategic insight
e.g. Kalman filter
e.g. with machine learning
e.g. rules engine
e.g. summary stats

17. Be as fast as you must.
But don’t be any faster
just for the sake of it.
Summary: IoT Data Analytics (I)

18. Data analytics can be a
deal sweetener!
39% of survey participants
are worried about the
upfront investment for an
industrial IoT solution.
CASE 1: Smart Parking

19. Westminster Parking Trial
https://www.westminster.gov.uk/new-trial-improve-conditions-disabled-drivers
IoT solution
service company
~750 independent parking
lots with a total of
>3,500 individual spaces
access to
Optimal sensor deployment

20. Optimal sensor deployment
labour:
expensive
sensor:
cheap
While the cost of the sensors is falling (and follows
Moore’s Law), digging them in and out for deployment
and maintenance is a significant cost factor.

21. Can we learn an optimal
deployment and sampling pattern?
•sampling rate of 5-10 min
•data over 2 weeks in May 2015
•overall 2.6 million data points
Can we make the customer’s budget go further by
• reducing the number of sensors in a geographic area?
• lowering the sampling rate for better battery life?

22. Good news: temporal occupancy
pattern roughly predicts neighbours
lots in Southampton
lots around
the corner of
each other
750 parking lots

23. A caveat: Is a high-degree of correlation
a function of parking lot size?
finding two lots of 20
spaces that correlate
finding two lots of 3
spaces that correlate
0:00 12:00 23:59
0:00 12:00 23:59
“more likely”
“less likely”

24. Bootstrapping in DBSCAN clusters
Simulation: Swap the occupancy vectors between parking
lots of similar size and test per grid cell if these lots still
correlate

25. Stratification strategy
3 lots with cc > 0.5
2 spaces
4 spaces
4 spaces
Test:
1. Take occupancy profile of
ONE random 2-space parking
lot and TWO random 4-space
parking lots.
2. Determine cc.
3. Repeat n times and get a cc
distribution for that parking lot
combination.

26. Combining stats with street knowledge

27. Even a temporary survey would have allowed us to make
a recommendation: 60% of the sensors at half the time
are effectively sufficient for the use case.
Summary: IoT Data Analytics (II)

28. Data analytics can be a
deal sweetener!
39% of survey participants
are worried about the
upfront investment for an
industrial IoT solution.
CASE 2: Asset Tracking

29. IoT - is it worth it?
The upgrade of a ‘dumb’ asset to
a ‘smart’ asset is an investment.
time,
money

30. Asset monitoring
base
Monday
WednesdayTraditional process
• small maintenance task
(if needed)
• weekly site visits to all
assets
• two independent tours
• time to reach asset is
main contributor to cost
• traffic-dependent

31. Data sources
Let’s assume the future isn’t going to be
much different than the past…
• log from past site visits: approx. likelihood for maintenance
• a collection of traffic data that’s somewhat representative

32. Log from previous visits
Monday tours
Wednesday
tours

33. Maintenance likelihood
• test for dependency
between Monday and
Wednesday tours
none
• test for dependency
within tours
none
The assumption of temporal
uniformity is reasonable.

34. Monte Carlo simulations
p1(need today)
patterns for a
demand-driven tour
‘cost function’:
sum of edges
base
default tour
base
p2(need today)
p3(need today)
p4(need today)
p5(need today)
p6(need today)

35. Travelling salesman problem
what’s the most
reasonable tour
from to ,
visiting all ?
heuristic search
is good enough,
but requires a
distance matrix

36. Traffic harvesting
• based on Google API
• generate a distribution
of travel times for each
edge in the graph,
dependent on time of
day (weekdays only)

37. IoT - is it worth it?
cost
awaiting
confirmation!
weeks
cost
weeks

38. Preliminary data taken from manual surveys, along with
‘open data’ and other sources can help making an
educated guess of the business value of an IoT solution.
Summary: IoT Data Analytics (III)

39. Dr. Boris Adryan
eMail: boad@zuehlke.com
Twitter: @BorisAdryan
www.linkedin.com/in/
borisadryan
Thank you!