artificial intelligence based six sigma for manufacturing...•solution scaled for target...
TRANSCRIPT
© GE Digital 2018, All Rights Reserved
Artificial Intelligence – Based Six Sigma for Manufacturing
Sameer Vittal, PhD
Sr. Director – Data & Analytics,
GE Digital – Global Services
November 2018
© GE Digital, 2018 – All Rights Reserved 312 April 2018Capgemini - GE Digital in Automotive |
Brilliant Factory
L E A N
A D V A N C E D
A D D I T I V E
D I G I T A L
Lean Continuous improvement
Dig
ita
lly E
na
ble
d O
pti
miz
ati
on
B A S I C
B R I G H T
B R I L L I A N T
© GE Digital, 2018 – All Rights Reserved 4
GE’s IIoT Software ApplicationsIm
pa
cts
K
PIs
Ou
tco
me
s
Industrial Application Platform
Operations
Performance
Management
Manufacturing
Execution Systems
Automation
HMI/SCADA
Improve operator productivity and reaction time through visibility and coordination of current operation’s state
✓ Visualization✓ Efficiency
OEE / shift
Personnel OEE
Product consumption
Alarm TTR
Defect rate
Improve production execution with less waste, less process disruption and greater process reliability
✓ Quality✓ Execution
Production volume
Production quality
Cost of goods sold
Increase revenue and margin by optimizing the performance of your process, plants, sites, and portfolio
✓ Process performance
✓ Operational
efficiency
Margin ($)
Production yield
Output
Increase asset reliability and availability while reducing asset-related cost and risk in operations
✓ Reliability
✓ Maintenance $
✓ Availability %
Unplanned downtime
Ops & Maintenance $
Safety
Asset Performance
Management
ServiceMax
Improve efficiency of mobile service personnel and provide visibility into the entire service delivery operation
✓ Technician
productivity
✓ Resource utilization
Work orders
completed
Customer satisfaction
Service revenues
© GE Digital, 2018 – All Rights Reserved 5
GE’s Point Of View : Manufacturing ExcellenceEnhance
Next Gen CapabilitiesMaintain & Improve
ISA 95
An
aly
tics
I
V
isu
aliz
ati
on
AGILITY
COST
SAFETY AWARENESS
WARRANTY
Increase Agility
Improve Quality
Increase Throughput
Deliver
Manufacturing Excellence
Awareness:
• Production monitoring
• Quality & Andon
Execution:
• Order Management and Execution
• Sequence & hold management
• Route management
• Broadcast management
• Material management (Kanban)
• Error-proofing (Poke Yoke)
Intelligence:
• Genealogy & Traceability
• KPIs, performance management,
and early warning
• Maintenance management & root
cause analysis
Sustainability:
• Energy Management
• Waste Management
© GE Digital, 2018 – All Rights Reserved 6
Analytics drives OEE Improvement
Overall Equipment
Effectiveness (OEE)
Reliability,
Availability &
Maintainability
Asset
Performance /
Yield
Product
Quality (6σ)
Operations, Maintenance, Inspection, Repair & Warranty data to understand the asset’s history & risks, FMEA’s
Exploratory Data Analysis and Data Mining to detect clusters & critical parameters
Advanced Reliability Analytics to forecasts events & risks
Reliability & Lifecycle Cost Optimization policies using discrete event simulation
Reliability & Lifecycle Cost Optimization Strategy
Risk Management Via Condition Monitoring
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
100
200
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0 50 100 150 200 250 300 350 400 450 5000
0.1
0.2
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500
1000
1500 0 1 2 3 4 5 6 7 8 9 101112131415161718
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0.5
1
1.5
Chamber Numbers
24-Hour Averaged Signal, All 18 Chambers
Frequency (Hz)
Magnitude
Clean sensor data & extract critical features using signal processing
Detect operating anomalies using physics, data & hybrid methods
Diagnose faults, provide mitigation advisories and predict remaining service life – while connected to lifecycle cost strategies
Machine SCADA & environmental data
© GE Digital, 2018 – All Rights Reserved 7
ML/AI Can Transform Traditional Six Sigma
6σ Phase Objective Traditional Tools Opportunities …. Where ML/AI can help
Define Define project goals and
customer deliverables
Project Charter, SIPOC, CTQ,
VoC, Process Flowcharts
▪ Look for opportunities … across thousands of sensor
streams and data sources (peer group segmentation,
clustering and multivariate outlier analysis)
Measure Quantify problems and
measure the process to
identify current performance
Process Capability (Z, Cp, Cpk),
MSA/Gauge R&R
▪ New measurement KPI’s to model complex processes
with 1000’s of measured parameters (numeric, acoustic,
images) using Machine Learning and Deep Learning
Analyze Analyze and determine root
cause of defects
Histograms, Pareto Charts, Run
Charts, Scatter Plots,
Regression, Cause/Effect
Fishbone, Hypothesis Testing
▪ Automated determination of vital X’s using pattern
recognition models – ML/AI adds to human capability
▪ Real Time and on-line learning models that adapt to
process changes (Manufacturing Digital Twins)
Improve Improve the process by
eliminating defects
Brainstorming, Poka Yoke,
DoE’s, Pugh Matrix, QFD, FMEA,
Simulation
▪ Learn from millions of prior cases – Case Based Reasoning,
Recommender Systems, Intelligent Search and Inference
(E.g. Diagnostics / Prognostics)
Control Control of future process
performance
Process Sigma / KPI’s
Control Charts
Control Plans / Processes
▪ Multimodal (Numeric, Images, etc.) , multi-sensor data
fusion based KPI surveillance and automated anomaly
detection and diagnosis
Machine Learning and Artificial Intelligence extract value from highly instrumented and connected assets
© GE Digital, 2018 – All Rights Reserved 8
New analytics methods … drive increased value
Use Case Description Applications Typical Analytics Methods Used
Generating
insights from
industrial data mining
Insight into asset risk
trends and fleet behavior
using sensors and other
transactional data
▪ Fleet Management – Top Issues and
Segmentation for Risk Mitigation
▪ Ideal for large customers … leverages
the power of their fleet’s data
Clustering (Latent Class, Hierarchic, K-Means, K-NN, Gaussian Mixtures,
Kohonen) • ANOVA • Multinomial Logit • Choice Models • Conjoint • Text
Mining (Topic and Sentiment Analysis); Exploratory Data Analysis • Hierarchic
Bayesian Linear Models • Principal Components & Factor Analysis • Time
Series Forecasting with exogenous variables (Smoothing, ARIMA, GARCH)
Anomaly
detection and
asset condition monitoring
Detect anomalous
behavior in turbines
using available streaming
sensor & configuration
data
▪ Condition Monitoring / Trending
▪ Detect Anomalies
▪ Diagnose Anomalies
▪ Prognosis of Remaining Service Life
Univariate/ Multivariate Robust Statistical Process Control •
Surrogate Models (Response Surfaces, Neural Nets, Kriging) • Similarity
Based Modeling & Kernel Regression • Machine Learning (Unsupervised, Semi
& Supervised Learning) • Signal processing (Fourier / Wavelets / Time-
Frequency) • AI - Deep Learning Neural Networks, Autoencoders, Log-Short
Term Memory / Recurrent networks, Transfer Learning, Extreme Learning
Asset lifecycle
cost, risk and
maintenance
optimization
Predict asset risk and
recommend optimal
maintenance strategies
▪ Reliability Centered Maintenance
▪ Condition Based Maintenance
▪ Lifecycle Planning (Spares, Logistics,
Crews and Inspection/Maintenance
Intervals)
Text Mining (Topic Analysis) of Maintenance Data • Survival/Reliability
Models (E.g. Weibull’s) • Regularized and Random Forest Weibull’s • Renewal
Models, Partial Repair • Non Homogenous Poisson Process • Weibull-
Regression • Accelerated Life Data models • Reliability Block Diagram •
Lifecycle Simulation with Sensors, Logistics and Scenario-based Optimization
Based on experience, we have developed a structured approach to link outcomes & use cases with the best analytics methods
© GE Digital, 2018 – All Rights Reserved 9
A structured process for industrial analytics
Workout Data ExplorationAnalytic
DevelopmentAnalytic
HardeningUser Acceptance
TestingProjectClosure
•Customer Needs Defined•Use Cases Identification•Data Status Established•Operating Mechanism Established•Core Team Established•Test Cases Identification for model V&V•User Acceptance Test criteria established
•Data Understanding Developed•Domain Knowledge Integrated•Hypotheses Defined•Minimum Viable Product (MVP) Scoped• Iteration Cadence & Phasing Agreed with Stakeholders
•Analysis options developed, scope refined as agreed with customer•Models developed, evaluated, refined and verified•Additional data identified, collected, integrated as needed and agreed•Model validation against test cases
•Solution scaled for target environment•Standard Operating Practicesestablished for process integration•Solution deployed, tested in target environment•Model made “platform ready” for cloud or on-premise deployment at customer end
•Solution tested by customer / end user(s)to User Acceptance Test criteria
•Ongoing Support Established as Agreed w/Customer•Project Artifacts & Documentation Integrated intoRepositories
Build-Measure-LearnFastworks Cadence
Our goal … is to help our customers “Cross the chasm” – between analytics proof of concepts and industrial operationalization
© GE Digital, 2018 – All Rights Reserved 10
Closing thoughts …
Industry 4.0 is driven by the confluence of machines, sensors and smart algorithms
Your manufacturing assets generate lot of valuable data – Machine Learning and Artificial Intelligence
(ML/AI) analytics are vital tools to make sense of this massive quantity of information
ML/AI are complementary to existing statistical methods and Six Sigma tools – can be easily added to
existing workflows and processes
ML/AI methods can help improve your asset’s Overall Equipment Effectiveness (OEE) by systematically
improving Reliability, Availability, Quality and (with controls optimization) Yield/Throughput
These methods provide a powerful toolkit to a digitally enabled Six Sigma professional
Samba DasariData Science Engagement [email protected]
+1.925.570.4723
Sameer Vittal, PhDSenior Director – Data & [email protected]
+1.678.699.3401
Please contact us for further information – we’re happy to help.