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Autonomous intelligence for the Industrial Internet - LibreCon 2016
This document discusses autonomous intelligence for the industrial internet. It notes that people no longer want gadgets but services that improve over time using personalized, context-aware, interactive, and real-time data. It then discusses how autonomous behavior monitoring, anomaly detection, pattern discovery, and real-time profiling can enable predictive maintenance, quality control, and equipment as a service for industrial assets using streaming machine learning approaches that can operate with limited computing power and connectivity in industrial internet of things environments.
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Autonomous intelligence for the Industrial Internet - LibreCon 2016
- 1. Autonomous Intelligence for the Industrial Internet Marco Laucelli Founder &CEO, marco@novelti.io Librecon 16, Bilbao November 22th, 2016 www.novelti.io
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- 4. www.amazon.com/Dash-Button People don’t want gadgetsanymore. They want services that improve over time.
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- 11. Performance Monitoring Predictive Maintenance QualityControl Equipment as a service
- 12. % Manual Expensive Today’s analytics processrequires a lot of manual efforts Outdated Models require frequent retraining andold data is useless High CAPEX and non-scalable tool-based approach
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- 14. Predictive Maintenance IndustrialAssets Physical inputs: vibration, temperature and pressure... Used supervised off-line trained models What is normal for a machine is environment-dependent Machines are continuously evolving
- 15. Can we providean continuous behavior learning system? Can we monitor the real-time behavior against the repository? Can we use behavior monitoring as an input for maintenance procedures? *Morales et al: Big Data Stream Mining Tutorial 2014
- 16. Autonomous· Behavior monitoring ·Anomaly detection · Pattern discovery · Real time profiling· SaaS
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- 20. The production goalof this system is to maintain the gas concentrations. The capacity is determined by the main compressor and the water pump flow. Fault aboutMay. Maintenance intervention. Continued to operate the machine for months until production loss was too high.
- 21. 0 5 10 15 20 25 30 31/7/13 31/8/13 30/9/1331/10/13 30/11/13 31/12/13 31/1/14 28/2/14 31/3/14 30/4/14 31/5/14 30/6/14 31/7/14 Caution 0 5 10 15 20 25 30 31/7/13 31/8/13 30/9/13 31/10/13 30/11/13 31/12/13 31/1/14 28/2/14 31/3/14 30/4/14 31/5/14 30/6/14 31/7/14 BH7 Maintenance Symptom evidence Final Failure 6 months
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- 23. Connected Cars Smart Manufacturing Home equipment Real time efficiency Connected Cars andM2M Industrial IoT Smart Home & Appliances Smart Metering and supply Asset PerformanceMonitoring Predictive Maintenance Quality Control
- 24. Real Time Autonomous analyticsfor Internet of Things Technical Challenges
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- 26. Devices expected tofail Limited computing power Limited connectivity Power limitations Expect failures
- 27. Periodical synchronization Out oforder delivery Globalclock Different data transfer patterns
- 28. Streaming Time windows ML stepssynchronization State-full or State-less Update and recalculate reports
- 30. Concluding remarks 1. IoTis 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.
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