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Copyright © 2016 by Cassantec Ltd.
This document is disclosed exclusively to the recipient.
Disclosure to a third party requires explicit, written permission of Cassantec Ltd.
Predictive Maintenance:
from condition monitoring
to condition-based prognoses
Forum Industrial Automation,
Hannover Messe, April 26, 2016
Copyright © 2016 by Cassantec Ltd. 1
Many decisions address the future but lack foresight
Cassantec provides that crucial prognostic information
“All condition
parameters are
currently in normal
range... Will they
stay in that range?
If not, then what?
“We have scheduled
the next overhaul for
our turbine in nine
months from today.
Is that too early?
Or maybe too late?
► The plan is that the turbine runs until the next scheduled outage.
► That plan is subject to high (technical and commercial) risk.
► That risk can be mitigated through a condition-based prognosis.
Copyright © 2016 by Cassantec Ltd. 2
PrognosticsDiagnosticsMonitoring
►Data generation
►Data illustration
►Data archiving
►Diagnostic insight
►Equipment-specific
focus
►Limited or no forecast
10+ Vendors100+ Vendors
►Prognostic foresight
►Scalable solution
►Significant, explicit
time horizon
Insightfor work orders
Alert / Alarmfor immediate response
Foresight for long-term planning
Prognostic foresight is provided by Cassantec
Copyright © 2016 by Cassantec Ltd. 3
Our online solution helps optimize maintenance and minimize downtime
a Optimize maintenance
b Minimize downtime
cEnable commercial
decisions
Example: turbine generator in power plant
Copyright © 2016 by Cassantec Ltd. 4
The value of Prognostics far exceeds that of Predictive Analytics
Predictive
Analytics
Prognostics
Today
Today
Warning
Know that
something will
happen at some
point in the
future
Know the
explicit time
window until
failure
?????? ???
Failure risk
t +1 t +2 t +3 t +4 t +5 t +6
t +1 t +2 t +3 t +4 t +5 t +6
Time
Time
Copyright © 2016 by Cassantec Ltd. 5
= × ×
Cassandra Markov Bayes Darwin
= × ×
Stochastic prognosis
(non-parametric model)
Automatic identification of
operating scenarios,
maintenance cycles and
data outliers
Stochastic inference model
Not rule-based, therefore
robust vis-à-vis unexpected
changes
The technology is a unique and patent-pending combination of advanced mathematical methods
PrognosticsParameter
prognosis
Malfunction
diagnosis
Fleet-wide
learning
Calculation of Remaining
Useful Life (RUL)
No physical modeling
necessary
Machine learning
on the basis of Markov
and Bayes algorithms
Copyright © 2016 by Cassantec Ltd. 6
Fle
et vie
wU
nit v
iew
Co
mp
on
en
tvie
wLogin
Fro
ntend
Solution Offering – Aggregated LevelThe reports cover the fleet, plants / units and critical main components
Copyright © 2016 by Cassantec Ltd. 7
The accuracy was confirmed via retrospective analysis– it increases over time through machine learning
Retrospective Analysis
► In retrospect, the predictable malfunctions were accurately predicted, with a horizon of up to 5 years (!)
► Operator knowledge was exceeded significantly, with several surprises (e.g. cartridge sealing, which the
operator assumed Cassantec would not find – analysis result on next page)
► Diagnostics und prognostics are enhanced over time through machine learning
June 2010 July 2010March 2009August 2008April 2007
Cartridge seals
Mech. seals
Cartridge seals
Mech. seals
Cartridge seals Coupling
Alignment
Coupling
Alignment
Mech. seal
Nov.
OK
Customer Example 1: coal-fired power plant
Copyright © 2016 by Cassantec Ltd. 8
Projection of washed out cartridge sealing (and steel casing) several years ahead
6 years
Prognostic Report for Feed Water Pumps
Post-mortemExample
Customer Example 1: coal-fired power plant
Copyright © 2016 by Cassantec Ltd. 9
Several levers for financial improvements are made available through Cassantec’s prognostic reports
► Execute work that is required technically and minimize extra efforts, while
maintaining the same risk profile
Reduced preventive scope and/or
frequency
Shift maintenance into low-cost
periods
Better maintenance work order
preparation
Preempt damages
Reduce redundancies
► Use longer advance notice to make work order preparation more efficient
► Identify malfunctions long before they develop into damages
► Tie up less capital in redundant assets, while maintaining the same risk profile
Reduce unscheduled maintenance
and/or repair
► Use foresight to bundle maintenance work that is visible on the time horizon
► Use prognostics to move maintenance from unscheduled to scheduled
Benefit lever (examples) Comment
► Plan the maintenance work in such a way that, for example, overtime and hiring
outside contractors can be avoided
Shift maintenance into low-revenue
periods
Knowledge management and
Benchmarking
► Use foresight to schedule maintenance work during low revenue times, e.g. when
load is expected to be low
► Increase employees’ efficiency through improved knowledge management
► Provide a basis to allow learning and best practices sharing
Enhance reputation ► Secure position as a quality, reliability and/or availability leader
Copyright © 2016 by Cassantec Ltd. 10
The correct prognosis of led to planned maintenance, avoiding unplanned downtime and damage
Prognostic Report for Cyclone Pump
Customer Example 2: Copper Mine
Report27 Aug ‘15
Report22 Sep ‘15
Decision:
• Change bearing assembly (26 Sep ‘15)
• Change gearbox (3 Nov ‘15) and oil (11 Nov ‘15)
Decision:
• Increase vigilance and observe development,
given that the current condition is green
Copyright © 2016 by Cassantec Ltd. 11
A Swiss run-of-river power plant uses Cassantec –GEN3 limits the plant’s RUL
Customer Example 3: Hydro Power Plant
Copyright © 2016 by Cassantec Ltd. 12
Trouble stems from the shaft
Customer Example 3: Hydro Power Plant
Copyright © 2016 by Cassantec Ltd. 13
Vibration increases at full-load, but RUL can be prolonged when capping operation at 80%
0 2000 4000 6000 8000 10000 12000 140000
20
40
60
80
100
120
140
160
180
Data Points
Vib
ratio
n V
1 R
ad
Y [
um
]
Time Series Linear Extrapolation Based on Mean Values
all Power
<= 7.6 MW
„The Prognostic Reports have
improved our daily plant management
and long-term planning. We can see
the effect today‘s decisions will have
on the entire plant‘s operations.
Through that we expect to lower our
cost as well as to increase reliability.“
Asset Manager
Prognostic scenario analysis reveals:
limiting the load to 7.6 MW eliminates further deterioration
Customer Example 3: Hydro Power Plant
Copyright © 2016 by Cassantec Ltd. 14
The configuration process absorbs very limited capacity on the customer side
Specify
malfunction
modes
► Definition
► Detection
► Response
Prioritize
malfunction
modes
Correlate
condition
parameters to
malfunction
modes
► Qualitative
► Quantitative
Specify/prioritize
equipment
Discuss
condition data
► Types
► Sources
► Intervals
Provide historical
condition data
► Specify
► Hand-over
► Review
Prioritize
& Discuss Data
Specify
Malfunction Modes
Configure front
end
► Equipm. view
► Unit view
► Fleet view
Customize
comput. model
Discuss results
► Specification
& assumptions
► Data time
series
► Implications
for asset mgt.
Batch
Specification
► Data sources
► Data format
► Time intervals
Configuration
and tests
Unit level:
Consideration of
forecasts in
Scheduling
Scoping
Preparation
of outages
Consideration of
forecasts in life
cycle and retrofit
decisions
Fleet level:
Consideration of
forecasts in
commercial
decisions
Configure
Solution
Automate
Data Transfer
Use
Forecasts
1 2 3 4 5
1 day
onsite
2 days
onsiteOngoing use
1 day
onsite (IT)
½ day
onsite½ day onsite 2 days onsiteCassantec-
internal1 day IT
Copyright © 2016 by Cassantec Ltd. 15
The team combines extensive technical andbusiness experience
Moritz von Plate, CEO
► Agricultural Engineer, University of Bonn
► MBA, Georgetown University
► Seven years with The Boston Consulting Group
► 2008–2012 CFO of Solarlite GmbH, an award-winning
pioneer in solar-thermal power generation
► Since 03/2013 CEO of Cassantec AG
Dr. Frank Kirschnick, CTO
► Computer Scientist, Technical University of Munich
► MSc, PhD, Stanford University
► Siemens Corporate R&D, industrial asset optimization
via Artificial Intelligence and “Big Data Analytics”
► Five years with Arthur D. Little
► In 2007 launched Cassantec AG
Copyright © 2016 by Cassantec Ltd. 16
Please contact us!
Company Profile
Cassantec AGTechnoparkstrasse 1
CH-8005 Zurich, Switzerland
Cassantec U.S. OfficeInsight Services Center
20338 Progress Drive
Cleveland, OH 44149, U.S.A.
Moritz von Plate, CEOT: +41 44 445 2260
F: +41 44 445 2261
C: +49 160 9486 5201
Dr. Frank Kirschnick, CTOT: +41 44 445 2260
F: +41 44 445 2261
C: +49 160 9774 3600
Management Team
Cassantec GmbHBismarckstr. 10-12
D-10625 Berlin, Germany
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