EcoStruxure
© 2019 Schneider Electric. All Rights Reserved.
EcoStruxure: ONE – OPEN - CYBERSECURE
Connected Products
Cyber Security
Analytics &
Services
Edge Control
Apps, Analytics &
Services
Connected
products
Edge Control
EcoStruxureTM
Building
EcoStruxureTM
EcoStruxureTM
EcoStruxureTM
Machine
EcoStruxureTM
Plant
EcoStruxureTM
Grid
Why Digital Services?
Key Offer Customer Benefits:
•
Increase employees and infrastructure protection by detecting early abnormal conditions (event avoidance)
•
Making the most of your investments by optimizing your assets lifetime
•
Achieve a better financial efficiency by optimizing your maintenance budget
Reactive
With EcoStruxureTM Asset Advisor you’ll
anticipate issues inside your electrical
distribution equipment and mitigate the risk of
electrical failure. This cloud-based service hosted
in Azure is supported by Schneider Electric
experts to provide actionable recommendations
and optional on-site support.
Data Monitoring
Applied
Analytics
Monitor
+
Analytics
+
Expertise
Predictive
Failure Event
Prescriptive
Services
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Holistic Power Systems Asset Management
Asset Depth
Analytics
- Artificial Intelligence
- Machine Learning
- Historical Analysis
- Power Quality Analysis
- Data Aggregation
Data Collected – Condition, Performance
- Operational: Overload trips, short circuit trips, ….
- Power: V, I, Power Factor, Dynamic, …
- Temp, Humidity, Partial Discharge
Dry
Transformers
Oil
Transformers
Transformers
MV
Switchgear
MV
Breaker
LV
Switchgear
Breakers & Switchgear
Asset Breadth
Customer Support
Customer Portal
Online Engineering
Consultants
Reports
LV
Breaker
Drives
Motors
Pumps
Conveyors
Rotating Equipment
Compressors
UPS
EcoStruxure Asset Advisor solution overview- From data to actionable insights
Get the best end-to-end service supported by experts that provide actionable recommendations and on-site support for
your electrical distribution system
TRANSFORMERS
CONNECT
ANALYZE
COLLECT
TAKE
ACTION
Convert data into
meaningful analytics
MV SWITCHGEARS
LV SWITCHBOARD
NOTIFY
MV/LV VSD
Notification in case of
abnormal behaviour
UPS
LV MOTORS
ENVIRONMENTAL
THERMAL MONITORING
Connect everything
from shop floor to
top floor
Capture critical data
at every level, from
sensor to cloud.
Drive action
through live
information and
business logic
VISUALIZE
Provide a synthetic
view of data
EcoStruxure Architecture maximizes the value of data
Transforms data from your
electrical distribution
assets…
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…through a cloudenabled anaytics &
expertise…
… into actionable insights and
recommendations from experienced
professionals to manage and maintain
equipment health
Technology + Human Experience
Critical Expertise
▪
Asset expertise – Deep knowledge of assets being
monitored within Global Online Engineering Centers.
Second line support from Global Experts, R&D,
Partners.
▪
Data, analytics expertise. Customer-specific
models and Machine Learning algorithms.
1 open platform constantly improving/upgrading
5 Global Online Engineering Monitoring Centers
~270 Sites + ~100 being connected in 2020
3,000+ AuM – Multi Type and Multi Brand
200+ Engineered Reports / year
~160 Asset Optimization reports YTD
▪
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Periodic Reporting
Incident Report
Motor Monitoring
Mechanical Failures
1. Bram Corne, Jos Knockaert, Jan Desmet, "Emulating bearing faults — A
novel approach", Electrical Machines (ICEM) 2016 XXII International
Conference on, pp. 2223-2229, 2016.
2. Bram Corne, Jos Knockaert, Jan Desmet, "Misalignment and unbalance
fault severity estimation using stator current measurements", Diagnostics for
Electrical Machines Power Electronics and Drives (SDEMPED) 2017 IEEE
11th International Symposium on, pp. 247-253, 2017.
3. Noureddine Bessous, S. E. Zouzou, Salim Sbaa, Wafa Bentrah, Z. Becer,
R. Ajgou, "Static eccentricity fault detection of induction motors using MVSA
MCSA and discrete wavelet transform (DWT)", Electrical Engineering Boumerdes (ICEE-B) 2017 5th International Conference on, pp. 1-10, 2017.
Motor Fault category
2-3 months warning
53%
Electric
47%
Electrical Failures
4. Noureddine Bessous, Salah Eddine Zouzou, Salim Sbaa, Abdellatif
Khelil, "New vision about the overlap frequencies in the MCSA-FFT
technique to diagnose the eccentricity fault in the induction
motors", Electrical Engineering - Boumerdes (ICEE-B) 2017 5th International
Conference on, pp. 1-6, 2017.
5. Tomasz Ciszewski, "Induction motor bearings diagnostic indicators based
on MCSA and normalized triple covariance", Diagnostics for Electrical
Machines Power Electronics and Drives (SDEMPED) 2017 IEEE 11th
International Symposium on, pp. 498-502, 2017.
6. Bram Corne, Bram Vervisch, Stijn Derammelaere, Jos Knockaert, Jan
Desmet, "Emulating single point bearing faults with the use of an active
magnetic bearing", Science Measurement & Technology IET, vol. 12, no. 1,
pp. 39-48, 2018.
Mechanic
3-6 months warning
How ESA technology works
Installation
Learning phase
Hardware to install
inside of Motor
Control Center (not
in motor), fitted in
small space under
safe conditions
Once installed, system starts to learn
assets’ specific patterns (Machine
learning)
1- 2 hrs. per motor
Sampling electrical waveforms
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2–6 weeks
Go live into Asset
Advisor!
Then, system monitors 24/7 and sends
notifications when upcoming failure is
detected creating a
reliable, accurate,
and easy to use
condition monitoring
solution that scales
Online Consulting Engineers
Critical Expertise
▪
500+ Sites on the platform
▪
Multi Type / Multi Brand Assets
▪
Asset expertise – Deep knowledge of assets being
monitored by asset EXPERTS.
▪
Data, analytics expertise. Customer-specific models.
Tuning of ML algorithms, historical and statistical analysis
Customer Partnership
▪
Knowledge of process-specific asset behavior
▪
Follow-up testing, interpretation of monitored data, incident
validation.
▪
Ongoing engagement w/ incident reports, annual APM
assessment, asset management recommendations.
Annual Report
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Incident Report
Deliverables: Incremental value
• Motor data automatically available in the Asset Advisor platform
• Specific dashboard: condition indicator, measurements
• Issues reported as recommendations via our Online Team
• Reports: Annual / Semi-Annual + incident reports.
Annual Report
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Incident Report
ESA Advantage
MCSA is an excellent substitute for most established technologies, Vibration. Why?
Highest failure detection rate
Works in harsh Environments
Among other advantages, MCSA has
significantly higher failure detection rate
than substitutes (e.g. vibration sensors)
Systems sensors - unlike any other
sensor technology - do not need to be
installed on the asset; inside safe MCC
instead. No sensors enter an ATEX zone
>9/10 failures detected,
very few false positives
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Easier to install and scalability
Identify Mechanical & Electrical issues
System analysis on how power factor,
energy consumption and harmonics
affect the machine health and
performance.
This is enabling energy savings and
performance optimization insights
Example Library of Predictable Failure Modes
Application or
component
Supply
Motors
Coupling/ gears
Failure mode or cause of failure
How far in advance
can we detect this?
Voltage/current unbalance
months
Current/voltage harmonic distortion
months
PQ issues
months
Voltage drops/overvoltage
months
Stator shorts (interturn/turn-to-turn)
weeks
Stator winding looseness
weeks
Electrical unbalance
months
Broken/loose rotor bars
months
Rotor eccentricities
months
Misalignment
weeks
Soft foot
weeks
Bearing degradation
weeks
Mechanical unbalance
weeks
Coupling eccentricity
weeks
Coupling unbalance
weeks
Broken/cracked gear teeth
months
Gear misalignment/eccentricities
weeks
Pulley unbalance
months
Belt/chain wear
months
Application or
component
Pumps
Conveyors
Mixers
Failure mode or cause of failure
13
How far in advance
can we detect this?
Cavitation
months
Mechanical unbalance
months
Impeller damage
months
Bearing degradation
indirect
Misalignment
weeks
Bearing degradation
weeks
Misalignment
weeks
Mechanical unbalance
weeks
Impeller damage
months
Misalignment
weeks
Mechanical unbalance
months
Bearing degradation
indirect
Rolls/mills
Mechanical unbalance
months
Compressors
Mechanical unbalance
weeks
Blowers/fans
Fault detection library is improving and
expanding continuously
Examples
Confidential Property of Schneider Electric |
The Technology – Motor Current Signature Analysis
Example: Shaft Misalignment
Healthy
Misalignment
Expected pattern versus observed pattern
▪ Plot depicts the current spectra of the healthy condition compared to the misaligned condition.
Pump – cavitation (1/1)
Anomaly detected
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1
6
Conveyor – unbalance (1/1)
Anomaly detected
Anomaly detected (same anomaly)
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1
7
Pump - broken rotor bar
1
8
Energy level increase on multiple
frequencies, the overall pattern is
unique for broken rotor bars
Pump - broken rotor bar RUL-estimation (2/2)
Status
Immediate action
Inspect
Under review
No issues
The picture shows gradual energy increase on the 33Hz frequency during the monitoring period. As seen in the previous
picture on multiple frequencies the current signal is distorted which explains the broken rotorbar.
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Examples for your reference
Use Case #1
Confidential Property of Schneider Electric |
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