NLP Data Science Project Presentation:Predicting Heart Disease with NLP Data ...
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1. Reduce Unscheduled Downtime with
Predictive Maintenance
Mike Reed, Manager of Engineering and Analytics, Schneider Electric
2. Digital Transformation
Industry 4.0 and IIoT transforming industrial companies
Immersive asset simulation &
virtual reality (VR) training
Contextual content on device of
choice and overall asset health
ranking
Condition-based, predictive, machine
learning and augmented reality (AR) for
improved decision making
Enforce best practices for data
collection & operator rounds
Enable analysis and
decision making
Design and commission
assets at the lowest cost
Produce safely and
profitably and meet
regulatory compliance
Ensure availability and
reliability of assets and
safety of workforce
Reduce Total Cost of
Ownership
Agnostic, to leverage existing
investments. Flexible deployment - on
premise, cloud, hybrid
Industry Imperatives Enabling Capabilities
Performance Losses &
Downtime
Reduce O&M Costs
Optimize Maintenance
Strategy
Overall Asset
Performance
Safety & Asset
Reliability
Resulting BenefitsTechnology Trends
IIoT & CONNECTIVITY
CLOUD & MOBILITY
BIG DATA & ANALYTICS
AR & VR
3. We are THE Leader in Industrial IoT
Digital Transformation to Maximize Return on Assets
2 Million
Licenses
Installed
100,000+
Sites Worldwide
20+
Billion
Operating
Parameters
10+
Trillion
Industrial
Transactions/Day
12+ Petabytes
of Data Stored/Year
4000 Ecosystem
Partners
Beyond traditional control and connectivity: Open and flexible operating modes, on premise, cloud and hybrid.
4. Strategic
(increased asset utilization,
improved performance and
better maintenance planning)
Operational
(reduce downtime, asset
utilization, improved quality,
production performance)
Engineering
(decision support, less-time
analyzing and more time
acting, mobile solutions)
Financial
(reduced operational and
maintenance costs)
Safety
(reduced risk, early warning of
impending catastrophic
equipment failures)
IT
(data quality, utilization,
improved models, real-time
and predictive insights)
Benefits of Predictive Maintenance
5. Achieve the Next Level of Maintenance Maturity
Apply the right strategy
Risk-based
Condition
Preventive
Predictive
Prescriptive
6. • Software based modeling of equipment using advanced
pattern recognition and machine learning
• IIoT Data Integration
• Predictive Asset Analytics Engine (Optics)
• Scalable Data Management Infrastructure
• Web Services API
• Alert Management
• Alert Notification
• Case Library
• Web Based Analysis and Visualization
Predictive Asset Analytics Software
9. Monitoring Approach
Traditional Monitoring
• Constant alert/alarm limits are
typical
• Damage accumulates prior to
reaching limit
• Actual minus estimated (residual)
signal detects anomaly as-soon-as-
possible
APR Monitoring
14. Predictive analytics is a key
part of a comprehensive
Enterprise Asset
Performance Management
(APM) solution. These
solutions connect vast
amounts of data, providing
context and analysis to
prescribe the appropriate
actions.
• Improve asset
performance
• Increase reliability and
reduce unscheduled
downtime
• Increase asset utilization
and extend equipment
life
• Reduce operations and
maintenance costs
Maximize Economic Return on Assets
Closed Loop Asset Performance Management
15. Maximize
Economic Return
on Assets
Organizations can maximize return on
their assets by adopting Enterprise
APM
Enterprise APM empowers
organizations to:
• Improve asset performance
• Increase reliability and reduce
unscheduled downtime
• Increase asset utilization and
extend equipment life
• Reduce operations and
maintenance costs
16. AEP uses predictive
analytics to detect turbine
damage & avoid forced
outage
Client:
American Electric Power
Location:
Columbus, Ohio
Closing the Loop:
Predictive analytics helped AEP use a
scheduled opportunity outage to repair a
damaged turbine engine. Prior to the
scheduled outage, the plant notified the
M&D center indicating a vibration sensor
anomaly and a change in one bearing.
AEP was able to use a planned outage
to investigate the turbine, and found a
chunk out of a stage 3 blade on the
compressor.
Results:
The solution enabled AEP to:
‣ Detect equipment failures before
they occur
‣ Prevent unscheduled system
downtime
‣ Harness real-time information that
brings plant operators and
engineering experts together to
collaborate
Predicting an
8-week
Unplanned outage
Using
Real-time Data
To detect unforeseen events
Saving
+17.5 Million
On reactive maintenance repairs
and unplanned downtime
$
17. Duke Energy Leverages
IIoT and Predictive Analytics
to Reduce Failure
Page 17Confidential Property of Schneider Electric |
Client:
Duke Energy
Location:
Charlotte, NC
Closing the Loop:
Duke Energy is a regulated and non
regulated utility with over 60+ plants in 6
states, including coal, simple cycle
combustion turbines, combined cycle and
integrated gasification combined cycle
plants. Duke Energy centrally monitors
power generation assets with predictive
analytics technology
Results:
Predictive analytics enabled Duke Energy
to:
‣ Empowering people with early warning
notification of equipment problems
‣ Optimizing assets with low-cost sensors
and connectivity for high-fidelity data
access enabling predictive maintenance
‣ Improving operations by providing
contextualized insights for smarter
decision making and efficiency
Serving
7.2 Million
Customers
Generating capacity of
58,200 MW
Saving
+7.5 Million
Due to early warning of a crack
in a turbine rotor
$
Total Savings: $31 million and growing
18. Air Liquide uses predictive
analytics to detect
compressor problems &
avoid forced outages
Client:
Air Liquide
Location:
Global
Closing the Loop:
Predictive maintenance helped Air
Liquide use a scheduled opportunity
outage to repair a damaged turbine
engine. Prior to the scheduled outage, the
plant notified the M&D center indicating a
vibration sensor anomaly Air Liquide was
able to use a planned outage to
investigate the compressor and found a
cracked impeller.
Results:
Predictive maintenance helps Air
Liquide:
‣ Detect equipment failures before they
occur
‣ Prevent unscheduled system
downtime
‣ Harness real-time information that
brings plant operators and
engineering experts together to
collaborate
Predicting a
Compressor
Failure
Using
Real-time Data
To detect unforeseen events
Saving
+500K
Preventing reactive maintenance
and unplanned downtime
$
