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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
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.
The Internet of Things
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
Only today!
Live and exclusive at C4IR:
Mostly NDA stu
ff.
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
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!
Predictive maintenance
Remaining useful lifetime
“some quantitative measure”
bad
goodtime
“critical”change here!
not here
definitely not here! f(t)
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
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) + …
data recording model building test use in production
data recording (production system)
evaluation
raw data clean-upfeature
engineeringmodel
learningmodel
selection
labour intense compute intensebrain intense
Machine learning pipeline
developmentproduction
The Internet of Things
data storage+compute
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
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
The same principles apply, even if it’s not strictly IoT
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
Be as fast as you must.
But don’t be any faster just for the sake of it.
Summary: IoT Data Analytics (I)
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
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
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.
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?
Good news: temporal occupancy pattern roughly predicts neighbours
lots in Southampton
lots around the corner of each other
750 parking lots
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”
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
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.
Combining stats with street knowledge
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)
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
IoT - is it worth it?
The upgrade of a ‘dumb’ asset to a ‘smart’ asset is an investment.
time, money
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
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
Log from previous visits
Monday tours
Wednesday tours
Maintenance likelihood
• test for dependency between Monday and Wednesday tours
none
• test for dependency within tours
none
The assumption of temporal uniformity is reasonable.
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)
Travelling salesman problem
what’s the most reasonable tour from to , visiting all ?
heuristic search is good enough, but requires a distance matrix
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)
IoT - is it worth it?
cost
awaiting confirmation!
weeks
cost
weeks
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)
Dr. Boris Adryan
eMail: [email protected]
Twitter: @BorisAdryan
www.linkedin.com/in/ borisadryan
Thank you!
