autonomous intelligence for the industrial internet - librecon 2016
TRANSCRIPT
AutonomousIntelligencefortheIndustrialInternet
Marco Laucelli Founder & CEO,
[email protected] 16, Bilbao November 22th, 2016 www.novelti.io
www.microduino.cc
IoT
www.amazon.com/Dash-Button
People don’t want gadgets anymore.They want services that improve over time.
PersonalizedContext-awareInteractiveReal-time
Service=Data
Usage-driven product design
Energy saving recommendation service
Usage-driven product design
Predictivemaintenance & sales
Energy saving recommendation service
Usage-driven product design
Performance Monitoring
Predictive Maintenance
Quality Control
Equipment as a service
1%
Manual
Expensive
Today’s analytics process requires a lot of manual efforts
OutdatedModels require frequent retraining and old data is useless
High CAPEX and non-scalable tool-based approach
New data sources
Unknown environments
New interactions
New business models
Predictive Maintenance Industrial AssetsPhysical inputs: vibration, temperature and pressure...
Used supervised off-line trained modelsWhat is normal for a machine is environment-dependent Machines are continuously evolving
Can we provide an continuous behavior learning system?
Can we monitor the real-time behavior against the repository?
Can we use behavior monitoringas an input for maintenance procedures?
*Morales et al: Big Data Stream Mining Tutorial
2014
Autonomous · Behavior monitoring · Anomaly detection · Pattern discovery · Real time profiling · SaaS
Plug
Lear
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15
Metrics & Alerts
The production goal of this system is to maintain the gas concentrations.
The capacity is determined by the main compressor and the water pump flow.
Fault about May. Maintenance intervention.
Continued to operate the machine for months until production loss was too high.
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Caution
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BH7
Maintenance
Symptom evidence
Final Failure
6 months
On-line Machine Learning
Real-time metrics and
alerts
Collective Intelligence*
Anomalies and
behaviours
Connected Cars
Smart Manufacturing
Home equipment
Real time efficiency
Connected Cars and M2M
Industrial IoT
Smart Home & Appliances
Smart Metering and supply
Asset Performance Monitoring Predictive Maintenance Quality Control
Real Time Autonomous analytics for Internet of Things
Technical Challenges
Streaming Machine Learning
What, When, How Long
Devices expected to fail
Limited computing powerLimited connectivityPower limitationsExpect failures
Periodical synchronization Out of order delivery Global clock
Different data transfer patterns
Streaming
Time windowsML steps synchronizationState-full or State-lessUpdate and recalculate reports
Concluding remarks
1. IoT is about everything becoming a web business. Flexible and fast business model innovation.
2. Analytics complexity will not stop growing. Adaptive learning is needed for IoT.
3. IoT data capture and quality will an issue and we need resilient ML approach.
Edge intelligence
Digital Twin integration
Collaborative Capture domain
expertise
Future trends
THANKS!Any [email protected]