phm frontiers in korean manufacturing – success episodes
TRANSCRIPT
Copyright © 2018 Laboratory for System Health and Risk Management in Seoul National University2018-04-13 - 1 -
YOUN, Byeng DongProfessor & CEO
System Health and Risk Management (SHRM) Laboratory (shrm.snu.ac.kr)Department of Mechanical and Aerospace Engineering
Seoul National University
OnePredict Inc. (onepredict.com)
NIST Workshop
PHM frontiers in Korean manufacturing –
success episodes and issues
Copyright © 2018 Laboratory for System Health and Risk Management in Seoul National University2018-04-13 - 2 -
Part Ⅰ: Smart Factory Activity in Korea01
Contents
PartⅡ: PHM Discipline and Activity02
PartⅢ: Successful PHM Episodes03
PartⅣ: Closing Remarks04
* PHM: Prognostics and Health Management
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4. Shunt Reactor
Part ⅠSmart Factory in Korea
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Manufacturing Power
100
99.5
93.9
80.4
76.7
75.8
72.9
69.5
68.7
68.4
China
United States
Germany
Japan
South Korea
United Kingdom
Taiwan
Mexico
Canada
Singapore
(100: High, 10: Low)
Ref: Delotte Touche Limited and US Council on Competitiveness, “2016 Global Manufacturing Competitiveness Index”
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Some Figures of Smart Factory Effort in Korea
Ref: ’18.04 MOTIE Smart Factory Korea Foundation “2017 Successful Episodes of Smart Factory”
• Number: 18 times from ’14 to ’17• Level: Elementary (76.4%)
277
‘14 ‘15 ‘16 ‘17 ‘20 ‘22
12402800
5003
12000
20000
•Elementary76.4%
Intermediate I21.5%
Intermediate II2.1%
<Number of smart factoriessupported by the KOST Program>
<The level of smart factories in domestic companies>
• Elementary: ERP with data acqn.• Intermediate I: RT equipment data acqn.• Intermediate II: RT decision making and
control
Korea Smart Factory Foundation (KOSF) Newly Launched in 2014
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Outcomes out of Smart Factory Effort in Korea
Productivity30% ↑
Manufacturing Cost
15% ↓Quality
45% ↑
Improvement of Overall Manufacturing Capability(2800 companies with smart factory, Dec 2017)
Downtime Cost16% ↓
Process Automation
DeviceMonitoring
Power Control
Risk Management
Life CycleManagement
Big DataAnalytics
Condition Monitoring
IoT
IoT
Product Monitoring
Industry 4.0 in Manufacturing
Ref: S&T Market Report Vol. 56 2018.02 Smart Factory Industry and Market Trend
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4. Shunt Reactor
Part ⅡPHM* Discipline and Activity
PHMOverview
SensingSolution
ReasoningSolution
DiagnosticsSolution
PrognosticsSolution
PHMArchitecture *PHM – Prognostics and Health Management
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Global Recognition of SNU & OnePredict
“Five times winner of Global PHM Data Challengesover Various Industrial Sectors”
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PHM Standard Architecture (Collab. w/ UNIST, KAU, and Kookmin Univ.)
Domain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
Dat
a Si
ze
Health Features
Raw Data
Feature Engineering
Preprocessing
Diagnostics/Prognostics
Health Prediction
Power & Energy
Hydro-system
Electrical &Electronics
Power Trans.
Machining
Driving
Cleaning Filtering
EnvelopingScalingStatistical Modeling
Organization
Domain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
Dat
a Si
zeDomain Knowledge
Data-driven
Rule-based Physics-based
Data-driven + Physics-based
(Hybrid)
Dat
a Si
ze
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PHM Standard Architecture
http://onepredict.com/blog/newsView.do
*M: MatureD: Developing
P: Promising
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4. Shunt Reactor
Part IIISuccessful PHM Episodes
Case Study 1Industry Robots
Case Study 2Industrial Bearing
Case Study 3Overhead HoistTransport
Case Study 4Deep Learning -Steam/Gas Turbine
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1. Steam Turbine2. Power Generator
3. Power Transformer4. Shunt Reactor
Industrial Bearing: Rolling-Element Bearing
Case Study 2Industrial Bearing
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REB Failure Prognostics (Schaeffler & Samsung Heavy Industry)
2. Analysis 3. Diagnosis & Prognosis
1. Sensing
ISO 10816-1 (Vibration measured on Non-Rotating Parts)
Waveform analysis Spectrum analysisEnvelope spectrum
& Feature extraction
0 100 200 300 400 500 600 700
time(hr)
1
1.1
1.2
1.3
1.4
1.5
prog
nost
ic in
dex
prognostics
Prognostic index & RUL Prediction
thresholdRUL
Learning Prediction
Industrial Bearings (custom order)
Wind Turbine Motor
Spindle Pump & Compressor
Robots
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1 Real-time Monitoring of Health Condition
2 Color Image of Health Condition
3 Trend Monitoring of Health Condition
4 Remaining LifeTrend Monitoring
“First-ever Commercial Solution for Bearing RUL Prediction”
REB Failure Prognostics (Schaeffler & Samsung Heavy Industry)
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1. Steam Turbine2. Power Generator
3. Power Transformer4. Shunt Reactor
Case Study 4Steam/Gas Turbine
Steam/Gas Turbine w/ Journal Bearing(Data-driven, Deep Learning)
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Autonomous machine learning algorithms to extract data features
through abstractionof massive data sets
Deep LearningHiddenLayer 1
𝑥𝑥2
𝑥𝑥1
…
𝑥𝑥𝑁𝑁
… …
…………
……
Inputlayer
…
……
RandomImages
HiddenLayer N
2012 2014 2016 2018
Year
0
20
40
60
80
100
Rel
ativ
e In
dex
World
Korea
Search Trends in Google
Popularity of Deep Learning- Improved computing power- Enlarged data size- Advanced DL techniques
Vision Recognition
Applications of Deep Learning
Voice Recognition
Healthcare PHM
Deep Learning
*R. Zhao et al. (2016), IEEE Tran. Neural Networks and Learning Systems (Submitted); K. Fragkiadaki (2012) Computer Vision-ECCV; Q. V. Le (2012) ICML
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DataAcquisition
FeatureExtraction
FeatureSelection
Feature Learning
Domain Knowledge Based Deep Learning Based
Domain Knowledge Required Not Required
System Dependency Dependent Independent
Feature Representation Shallow Deep
Big Data Learning Over-fitted Model Well-fitted Model
DataAcquisition Autonomous Feature Engineering by DL
DL Type Input Data Type Input Data Labels Others
DBN n/a Unsupervised Break through in deep learning (2006)
CNN Vision Data (Images) Supervised Parameter sharing, local connectivity
RNN Sequential Data (Speech) Supervised Storing sequential information
AE n/a Unsupervised Representation learning, dimension reduction
Deep Learning in PHM
*H. Oh et al. (2017), IEEE Tran. Industrial Electronics** R. Zhao et al. (2016), IEEE Tran. Neural Networks and Learning Systems (Submitted)
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Deep Belief Network
Convolutional Neural Network
Recurrent Neural Network
Auto Encoder
RBM 1 RBM 3RBM 2 RBM 4
2nd layer shared weights
1st layer shared weights Encoder Decoder
Code
Input Output
OutputInput Output Input
Input Output
RNN Cell
Deep Learning in PHM
*THE ASIMOV INSTITUTE (http://www.asimovinstitute.org)
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Deep Learning for Steam Turbine
2. Analysis
3. Scale-free Turbine Prognostics (Fully validated, 92% prediction accuracy)
1. Sensing
Gap Sensor
(Gap)HousingShaft
Vibration Data Acquisition
Vibration Image
Vib-Imaging Technique
Fault Log
# Norm. Rubb. Misalign. Oil Whirl
#1 0.975 0.010 0.014 0.006
#2 0.991 0.005 0.004 0.000
#3 0.482 0.288 0.186 0.042
#4 0.828 0.135 0.034 0.003
#5 0.037 0.958 0.005 0.001
# Status
#1 Normal
#2 Normal
#3 Normal
#4 Normal
#5 Rubbing
Deep Learning Result
vs.
Omni-directional Regeneration (ODR)
HP IP LP Gen. Ext.
Brg. #1 #2 #3 #4 #5
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4. Shunt Reactor
Part ⅣClosing Remarks
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Industrial Information Prognosis (IIP)
“Industrial Information is the one with industrial value, which includes availability, quality, productivity, energy efficiency, safety, etc.”
Conventional Business: Raw Material Product
RawMaterial Product
IndustrialRaw Data
IndustrialInformation
New Business: Industrial Data Information
Copyright © 2018 Laboratory for System Health and Risk Management in Seoul National University2018-04-13 - 28 -
Issues in PHM
01
03
02
04
?
Data QualityLack of Labeled Data
Cyber Security & Data OwnershipLack of Resources
Missing, Unsynchronized, Noisy Data
Class Imbalance, Labeling Quality
PHM Experts, Budgets
Protection of Cloud Network Information
Copyright © 2018 Laboratory for System Health and Risk Management in Seoul National University2018-04-13 - 29 -
1. Steam Turbine2. Power Generator
3. Power Transformer4. Shunt Reactor
THANK YOUFOR LISTENING
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