October 2018
Jim ChappellVice President – Information Solutions
Asset Performance Management
Predictive Analytics / Artificial Intelligence
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
ASSET
Value chain
OPERATIONS Value chain
Complete digital definition across all elements of design, engineering, construction and production
Complete digital definition and orchestration of all elements across
operations & maintenance
ASSET
PERFORMANCE
MONITOR
AND CONTROL
ENGINEER
PROCURE
CONSTRUCT
OPERATE
AND OPTIMISE
PLAN AND
SCHEDULE
ASSET
PERFORMANCE
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Assets are the Heart of the Business
• Insight into risks - Balance between financial
operational and safety risks
• Ever-changing legislation - Complying with
environmental and social requirements
• Sharing knowledge - Implement best practices
globally
• Continuous improvement – to drive maximum
return on industrial assets
RISK-BASED OVER-MAINTAINED
Goal
Cost of non-availability (production loss + break down)
Preventive maintenance cost
Total cost
Comfort mode
UNDER-MAINTAINED
Fire fight mode
Optimum Too muchNot enough effort
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Digital Lifecycle
Simulation
Design
Construct
Requirements
Commission
Preventive, Condition & Predictive Maintenance
Control of Work
Overall Asset Health & Risk
Operational Records & Shift Handover
Digital Maintenance Plan (Costs, History…)
A trusted digital asset in contextFor every physical asset
Common Asset Repository
Common Asset View
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Connect ActAnalyzeCollect
Real-time Integration, IOT
Operations Data
Engineering Data
Inspection Data
Predictive Asset AnalyticsBusiness Intelligence
EAM, CM, Control of Work
Advance Visualization AR/VR
Workflow, ProceduresRisk & ReliabilityTools
Asset Performance Management
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Performance Management
Supervisory Control, IOT, etc.
Maintenance
Analytics
Supervisory Control, IOT, etc.
Information
Industrial Big Data
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Performance Management
Supervisory Control, IOT, etc.
Maintenance
Analytics
Supervisory Control, IOT, etc.
Information
Industrial Big Data
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Hybrid
…
SECURE
Industrial Big Data
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Performance Management
Supervisory Control, IOT, etc.
Maintenance
Analytics
Supervisory Control, IOT, etc.
Information
Industrial Big Data
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Information
Cloud
On Premise
Hybrid
Information
Historical Real-time Engineering
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Information
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Information
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Information
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Alarms & Events
Information
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Alarms & Events
Information
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Alarms & Events
Information
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Condition-based Alerts
Information
Trigger Work Orders
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Information
Auto-generatedCustom Graphics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Information
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Performance Management
Supervisory Control, IOT, etc.
Maintenance
Analytics
Supervisory Control, IOT, etc.
Information
Industrial Big Data
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
• Gain insights
• Help in decision-making
• Be alerted of possible issues
• Quickly pinpoint root cause
• Recommend actions
• Improve operations & maint.
Condition-
based
Performance
Business
Intelligence
Machine
Learning
Predictive
Anomaly
Detection
Prescriptive
Prognostics Operational
“News”
Analytics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
• Real-time condition-based management
• Early warning detection of failures
• Connect control, safety, & maintenance environments
• Trigger work orders
• Reduce downtime
• Advanced calculations
Avantis.PRO
SAP
Maximo
Other EAMs
Condition-based
Analytics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Multi-Dimensional Analysis
Costs, etc…
Batch
Energy
Usage
Production
Data
Time/ Shifts
Equip states
Business Intelligence
Analytics
HQ
• Cloud • SaaS • Insight
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Machine Learning
Analytics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
• Anomaly detection
• Flatlined data
• Changes in value, cycle times, behavior…
• Interrelated tags (e.g., temp, press, flow)
• Integration with alerts, team collaboration…
• Zero configuration
• Significance score
• Variable threshold based on news volume
• Weighted based on user interests
Newsfeed
Machine Learning
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Machine Learning
Analytics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
EARLY WARNING DETECTIONDeviations from normal operation
identified and displayed3
Predictive
HISTORICAL DATAApplication learns normal
operation from historical data1
PATTERN RECOGNITIONAdvanced algorithms automatically create
and organize operational profiles2
• Automated model building
• Automated retraining
• Automated data cleansing
• Cloud
• MaaS
• SaaS
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Machine Learning
Analytics
Predictive
Prescriptive
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Prescriptive
• Signal impact
• Pinpoint problem
• Root cause analysis
• Upstream/downstream
line impacts
• Case management
Likely Fault Condition Probability of Fault Match
Asset Health Trend
Signal Contribution to
Change in Health Status
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Machine Learning
Analytics
Predictive
Prescriptive
Prognostics
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
• How fast will the situation
worsen?
• What-if analysis
• Can you make it to the next
planned maintenance outage?
ACTUAL data
stops here
PREDICTED future values;
there is no actual value at
these times
Prognostics
➢ Neural Net ➢ Deep Learning ➢ Easy to Use
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset Performance Management
Supervisory Control, IOT, etc.
Maintenance
Analytics
Supervisory Control, IOT, etc.
Information
Industrial Big Data
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Information & Analytics for Improved
Operations & Maintenance
Maintenance
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Maintenance
2015 2016 2017
AVEVA EAM Insight
(Incremental thru 2019)
AVEVA EAM
Modernization
(Quarterly Deliveries)
EAM Extensions
Mobile Maintenance, Mobile
Inventory, Data Pilot
Condition Management
Cloud enablement
Maintenance
Analytics &
Dashboards
Cloud
Maintenance MRO Inventory Procurement
& Invoicing
Application Modernization
Mobile
2018 . . .
Approvals CoW, Engage, AR/VR
EAM Product Enhancements
Integrations and Enhancements
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Mobile Workforce (IntelaTrac)
Maintenance
Enterprise
2017 202020192018
Thin Client
Cloud
Multi-platform
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Putting it all together
AVEVA Insight
Cloud Mobile
IIoT
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Cloud
On Premise
Hybrid
Simple – Intuitive – Frictionless
AVEVA Insight
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Asset / Entity
Raw or
production
material
Prognostics
Calculations
Alarms &
events
Condition
triggers
Video
Prescriptive
actionsAsset
health
Performance
OEE
HMI/
Process
Graphics
Sensor
data
Location
Predictive
alerts
Common IntelligentModel
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Integrated Digital Asset (Cloud)
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Technician makes repairs and closes out Work Order > Case Management
System recommends procedure to rectify, providing step-by-step
video
> Prescriptive
> Mobile Workforce
System informs technician the pump is likely to fail within 7 days.
Emergency Work Order issued.> Prognostics
Technician finds oil reservoir filled with half water / half oil.
Determines valving supplying too much pressure to the seals,
resulting in water flowing to the bearings.
Real-world >> Valving Issue
System triggers Work Request> Condition-based
> EAM
Technician receives alert on mobile device > Mobile
System determines bearing temp too high for conditions > Predictive
Insight collects bearing sensor data in the cloud > Big data
> Troubleshoot
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Unique Offers
Common
Features
The Familiarity and Benefits of Each Application
Award-winning Customer First Service
Component of the Largest Industrial Software Portfolio
On-premises
On-premise
Full-featured
Perpetual or
Subscription
Standalone
Cloud (CEP)
Native cloud
Full-featured
SaaS Model
Single Tenant
AVEVA
Insight
Native cloud
Easy to Consume Apps
SaaS Model
Multi-tenant
APM Suite of Products
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Advanced Analytics Deep-dive
Advanced Analytics
What’s Happening
Real-Time
• Processing of real-time operational data
• Rule based inference for causal analysis
Real-Time Domain
What Happened
Historical
• Assessment and exploration of historical operational data
• Trends, KPIs, Dashboards to present abstracted views
Historical Domain
What If
Predictive
• Comprehensive model based assessment of operational data ranges to determine potential outcomes.
• Deterministic or non-deterministic models
• Open-loop simulations
Science DomainWhat to do
Prescriptive
• Systems that synthesize, predict and provide scenario-based guidance
• Fault diagnostics and knowledge capture
• Sensor contribution & root-cause analysis
Maintenance Action Domain
How bad will it get
Prognostics
• Forecast future state of assets and sensor values
• Determine if you can make it to the next planned maintenance outage
• Provide key input for risk assessment
Artificial Intelligence Domain
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
▲ Software based modeling of equipment using
advanced pattern recognition
▲ Uses historical data to describe how a piece of
equipment normally operates and build a model
▲ Continuously monitors behavior in real-time
▲ Alerts when the operation differs from the
historical norm
▲ Early warning detection of equipment problems
▲ Advanced analysis capabilities including problem
identification and root cause analysis
Predictive Asset Analytics Software
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Monitoring without advanced analytics
Plot-0
4/6/2008 4:39:21.791 PM 4/9/2008 4:39:21.791 PM3.00 days
60
62
64
66
68
70
72
74
76
58
78
96
108
86
102
107
113
94
102
46
64
0
14
110
170
52
70
25
75
Subtle Changes
from normal asset signature
Actual Value
Predicted ValueOu
tbo
ard
Be
arin
g T
em
p (
°F)
Date and Time
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
A Smaller Box
▲ Group data into smaller modes of operation
Ou
tbo
ard
Be
arin
g T
em
p (
°F)
Oil Drain Temp (°F)
Traditional Alarms
PRiSM
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Case Management
Core Functions of Avantis PRiSM
Early Warning Event
Detection & Management
(Alarm Manager)
Trend Analysis
Diagnostics Advisor
(Fault Diagnostics)
Reporting
Significance of the Deviation from Normal
Operation
Signal Contribution to Performance Anomaly – Normal/Predicted vs.
Actual
Likely Fault Condition
Signal Contribution to
Change in Health Status
Probability of Fault Match
Asset Health Trend
Asset Health Monitoring
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Make new templates or
projects based on templates
Model Building
Easily Create New Models
From Scratch
Analytical Visualization
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Intelligent Alert List
Case Library▲ Knowledge Capture
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Data Playback
▲ Can have “n” number of training
data sets
▲ Determine when PRiSM would
catch the problems
▲ Determine when to alarm
▲ Easy to retrain model
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Transient Analysis
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Prescriptive DiagnosticsLikely Fault
Condition
Signal Contribution to Change in
Health Status
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Vertical Markets and Equipment Types
▲ Turbine
▲ Compressor
▲ Electric Generator
▲ Pumps – Centrifugal, Integral
▲ VFD’s
▲ Fans, blowers
▲ Heat Exchanger, Boiler, Oven, Kiln
▲ Air Heaters
▲ Water Heaters
▲ Pulverizer, Crusher
▲ Condenser
▲ Transformers, Breakers, Capacitors
▲ Agitators, blender, Mixer
▲ Gearbox
▲ Chillers
▲ Seal systems
Power Generation
Power T&D
Oil & GasWater Management
Mining Process
Manufacturing & F&B
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Types of Algorithms
• OPTiCS
• Soft Sensors
• Automated Data Cleansing
• KANN (RNN)
• Entropy
• Deep Learning (LSTM)
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Real World
Predictive model
• Top temp of each zone
• Bottom temp of each zone
• Damper valve %
• Extraction fan speed
• Etc…
• Zone humidity
• Zone pressure
• Extraction fan current
• Oxidizer pressure
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Issue
• Each oven zone needs to maintain certain heating
characteristics to drive product specifications, such as stack
height, color, % moisture content, etc. Predictive analytics
is used to minimize line stoppages.
Real world: Ovens
Early Warning Prevents Pump From Failure
Example PRiSM Catch
Observation:
Model is indicating an increase in vibration on multiple
bearings for the given flow rate and pressure
Result:
A coupling shim pack that was on the verge of failure
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Catch PRiSM identified a conveyor motor drawing a higher than expected amount of
current (amps) for the given level of operation.
Cause In an effort to improve tracking, a mechanic changed the tension of the belt by
increasing the air pressure.
Avoided impacts If this hadn’t been caught, a bearing, roller, motor, or some
combination of these would have been compromised, resulting in significant downtime.
In addition, the belt would have likely separated.
Case Study: Dough Laminator
Issue Monitor dough quality by detecting multi-variate sensor changes which could indicate a
problem with a motor, gearbox, or bearing.
Predictive Models
Roller Mechanical
• Amps drawn by motors
• Speed of roller motors
• Gap between top & bottom rollers
Conveyor Mechanical
• Amps drawn by conveyor motors
• Speed of conveyor motors
• Speed of production line
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Catch The system identified a higher than expected differential pressure
across the catalyst bed, indicating a clog which can disrupt the oxidation
process. However, this issue only occurred once in awhile.
Cause The site determined that this predictive notification only occurred
when the weather was extremely cold, causing the lines that bleed
moisture to the outside to freeze. Maintenance personnel unthawed the
lines, allowing the moisture to dissipate which, in turn, reduced the
pressure and eliminated the predictive analytics alarm.
Case Study: Oxidizer
Issue The oven oxidizer is responsible for reducing the environmentally
harmful emissions created during the baking process. This is done by
burning the oven exhaust gases at a high temperature (oxidizing) and then
running the hot exhaust over a catalyst to help induce a chemical reaction.
Predictive Models
Oxidizer Combustion Chamber
• Differential pressures
• Air temperatures
• Damper and thermal settings
Oxidizer Fan Inlet
• Amps from fan motor
• Differential pressures generated
• Temperature of oven exhaust
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Case Study: Pump Control Issue
Example PRiSM Catch
Observation:
• Vacuum fell from 27.8 to 25.9 inches within an hour
• Hotwell temp increased from 98 degrees F to 123 degrees F within an
hour
Result:
Found water in the air lines to the suction valves at the vacuum pumps,
which was causing them to close
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Case Study: Irregular Motor Operation
Example PRiSM Catch
Observation:
Motor current increased from 14 amps to 18 amps for a given load.
Result:
Plant found a leak on the floor above that saturated the Air Heater
insulation, causing expansion issues with the shroud
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Issue
• Pasta production line creates methane gas which is used as
fuel in a cogen facility. The plant uses the methane to
generate steam in order to produce heat and electricity, both of
which are subsequently used back in the production process.
Predictive analytics is used to optimize maintenance practices
to avoid unplanned outages.
Predictive model
• Inlet/outlet water temps
• Inlet/outlet water pressures
• Inlet/outlet steam temps
• Inlet/outlet steam pressures
• Etc…
• Pump current
• Pump flowrate
• Boiler water level
• Fan current
Case Study: Cogeneration
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
PRiSM Catch
Observation:• Unit started after an outage
• Vibration step change on a LP turbine
• Notified Engineering & Plant
• Vibration data was collected & unit was retired for inspection
• Bolts on lower half of flow sleeve broke & flow sleeve contacted L-0 blades
Result:• Upper half of flow sleeve was no longer supported by lower half
• Avoided damaging multiple stages of blades, packing and diaphragms
• Est. Cost Avoided: $4.1 million USD
Outage
Case Study: Major Steam Turbine Catch
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
•31,000 MW; over 60 generating stations
•60% coal, 23% natural gas, plus nuclear, wind, hydro, and pumped storage.
•A collaborative work effort between AEP’s M&D Center, the plant, and the
engineering group resulted in several major turbine issues being identified and
corrected early, before much more devastating results occurred.
Summary of AEP
Case Study: Gas Turbine
Sample PRiSM Catches
Observation: Unaccounted for turbine vibrations for the given generation level
Result: After investigation, a turbine blade was found to have a chip that was
reducing the efficiency. This issue was getting worse over time and would have
eventually resulted in a failure.
Savings: The plant replaced the blade and estimated the cost avoidance at over
$17million (2016 PRiSM catch of the year)
Observation: A gas turbine bearing vibration had changed from 0.37 in/sec to
0.46 in/sec.
Result: During a planned outage, the plant found a chunk out of one of the stage
3 blades on the compressor.
Savings: The plant replaced blading on row 3, resulting in an estimated cost
savings of $1,500,000
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Case Study: Transformer
PRiSM Catch
Observation:• Southern Company found a major transformer issue during Hurricane Matthew in October
2016. One of the PRiSM transformer health indicators (dissolved gas ratios) went extremely
high, indicating problems. Southern investigated, finding a transformer that was fully charged
with no load.
Result:• An explosion could have occurred in this situation if loads were brought on quickly. They
successfully prevented catastrophe. Typical replacement cost of a major transmission
transformer ($10 million USD).
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Catch: System identified higher than expected vibrations on the second stage of
a compressor in relation to the current level of operation. Based on this detected
anomaly, the site investigated the issue during an upcoming planned maintenance
outage.
Cause: The site found a cracked impeller.
Avoided impacts: The customer estimated that over $500K in costs were
avoided by preventing reactive maintenance and unplanned downtime. Based on
the speed of the crack propagation, our Predictive Analytics detected this issue
approximately 3 months before operators would have noticed it.
Case Study: Compressor
Issue: Compressors are extensively used and critical to the operation of
one of the largest manufacturers of industrial gases in the world.
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Line 8 Oven• Tag determining if the oven is ON/OFF
• Pounds per minute produced while running
Line 8 Accumulator• Percent fullness of accumulator
• Max capacity of accumulator in pounds
Packing Line 13• Tag ON/OFF
• Lbs packed per minute
Packing Line 15• Tag ON/OFF
• Lbs packed per minute
Packing Line 14• Tag ON/OFF
• Lbs packed per minute
Packing Line 16• Tag ON/OFF
• Lbs packed per minute
Packing Line 11• Tag ON/OFF
• Lbs packed per min
Packing Line 12• Tag ON/OFF
• Lbs packed per min
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Line Monitoring – Upstream/Downstream
Periods of increasing
accumulator levels
correspond with depressions
in the ‘Time Full’ chart.
Percent Fullness
of Accumulator
Time Left Until
Full Accumulator
Alarms typically configured for
when the ‘Time Full’ tag is less
than 5 minutes
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Line Monitoring – Upstream/Downstream
Benefits and Costs Avoided
with Predictive Analytics
1. Reduce Unscheduled Downtime
2. Prevent Equipment Failures
3. Reduce Maintenance Costs
4. Improve Safety
5. Increase Asset Utilization
6. Extend Equipment Life
7. Identify Underperforming Assets
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Integration with InTouch or System Platform
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Open Integration
Any Data
Historian
Any
Control
System
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Cloud Integration
Wonderware Online
Any
Control
System
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Monitoring Diagnostics & Services Center (MDSC)
▲ Located in Chicago, IL
▲ Analytics Manager & Domain Experts
▲ Program Manager
▲ Subject Matter Experts (SMEs)
▲ Supporting Engineers with Industry Expertise
▲ 24x7 software monitoring with 8x5 analyst staffing
▲ Services offered:
▲ Model Building Services
▲ Monitoring Services
▲ Training Services
▲ Consulting Services
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
Questions?
Challenge/Opportunity
▲ Due to the advanced pattern recognition and alarming features of the Avantis PRiSM software, catastrophic equipment
damage and potential significant personnel injury were averted.
Duke Energy Steam Turbine
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.
SE Products Used
▲ PRiSM
Results
▲ After reviewing the events and all actions taken to remedy the situation, a conservative estimate
of the equipment Avoided Costs was determined to be approximately $34.5 Million.
Duke Energy Steam Turbine
© 2018 AVEVA Solutions Limited and its subsidiaries. All rights reserved.