Overview of an Embedded Platform for Big Data in Manufacturing

This presentation was given at JavaOne Embedded 2012 in San Francisco. The talk goes over what is needed in an embedded platform to enable big data analytics in manufacturing.

Transcript

1. system insights © System Insights, 2012 Overview of an Embedded Platform for Big Data in Manufacturing Athulan Vijayaraghavan, Ph.D CTO System Insights Thursday, October 4, 12
2. © System Insights, 2012 system insights System Insights Thursday, October 4, 12
3. © System Insights, 2012 system insights System Insights Software for Manufacturing Big Data Analytics Monitor à Detect à Predict Realtime Analysis of Integrated Manufacturing Systems Machine Learning for Historical Analysis Analytics Platform system insights Thursday, October 4, 12
4. © System Insights, 2012 system insights Manufacturing Big Data Thursday, October 4, 12
5. © System Insights, 2012 system insights Why do we care? Manufacturing is Big: $2 Trillion sector Discrete Manufacturing: Products for consumers and the supply chain High potential for productivity improvement: Metal cutting equipment spend less than 25% usefully Manufacturing generates a very large amount of data: Most of this falls on the floor Thursday, October 4, 12
6. © System Insights, 2012 system insights Why do we care? transformation How well are we making this transformation? Productivity? Profitability? Return on asset? Part quality? Employee safety? Product reliability? Sustainability? Energy usage? Pollution? Thursday, October 4, 12
7. © System Insights, 2012 system insights McKinsey on Manufacturing Big Data Huge potential for Big Data in Discrete Manufacturing McKinsey Global Institute Analysis Thursday, October 4, 12
8. system insights © System Insights, 2012 Focus Area: Discrete Manufacturing Large facilities with disparate types of equipment Limited automation: Human driven and controlled Very low computational power. Average equipment > 10 years old Unintegrated “islands of excellence” http://www.michsci.com/Services/manufacturing.htm Thursday, October 4, 12
9. © System Insights, 2012 system insights What kind of data? spindle speed position alarms notifications static data acoustics tribal knowledge vibrations temperature quality Thursday, October 4, 12
10. © System Insights, 2012 system insights Temporal Decision Scales Temporal scales can vary from µ-seconds to days anytime: process management neartime: process improvement realtime: process control m-Seconds Seconds Hours Days Process Interface Sub- Components Manufacturing Equipment Manufacturing Supply Chain Manufacturing Enterprise Temporal Decision Scale Manufacturing Analysis Scale Thursday, October 4, 12
11. © System Insights, 2012 system insights Discrete Manufacturing: How Much Data? Small Shops: 2~10 TB/year Medium Shop: 5 ~ 25 TB/year Large Shop: 16 ~ 80 TB/year Enterprise: 80 ~ 4000 TB/year 200 PB ~ 950 PB / YEAR Thursday, October 4, 12
12. system insights © System Insights, 2012 Grand Challenges Enterprise Traceability vimana Transfer Station Transfer Station Part Part Part Process Station Process Station Realtime Part Monitoring and Process History OEM Sourcing Metrology Station Supply Chain Management Long Term Regulatory Records Thursday, October 4, 12
13. system insights © System Insights, 2012 Grand Challenges Historical Consumption Patterns Feedback and Refinement Process Planning Process Execution Process-based Reasoning Continuous Improvement with vimana Energy Monitoring using Java SE Embedded Process Advisory Services Energy Efficient Execution Energy Management Thursday, October 4, 12
14. © System Insights, 2012 system insights A Big Data Stack Thursday, October 4, 12
15. © System Insights, 2012 system insights Complex Reasoning Engine Data Management Framework Data Collection Data Storage Data Analysis Big Data Apps Efficiently capture and deliver data from the manufacturing equipment to the collection systems Perform reliable realtime analysis using statistical machine learning (SML) and complex event processing (CEP) Robustly collect and store data in high- speed databases Thursday, October 4, 12
16. © System Insights, 2012 system insights vimana: Complex Reasoning Platform Platform for manufacturing productivity management and improvement High Speed Data Bus High Speed – High Availability Data Capture User Facility User Facility User Facility CRE: Pattern Matching and Anomaly Detection High Speed Process Execution Database MLE: Learning and Refinement 3rd Party Apps User Data Anonymizer User Interfaces vimana Platform Thursday, October 4, 12
17. For pricing and availability, please contact: sales@systeminsights.com | © System Insights, 2012 Productivity Monitoring vimana user-interface Keeps track of time spent by the device in production activities and duration of production losses Auto-classify production losses into reasons, including: setup, breakdowns, tool change, program adjustment. Data Sources for Auto- Classification: •CNC, PLCs, Sensors •User Input, including Device and Plant Schedule Supported Metrics: •Production Efficiency •Part Count •Cycle Time •OEE •Energy Usage Thursday, October 4, 12
18. For pricing and availability, please contact: sales@systeminsights.com | © System Insights, 2012 Detailed Reports Powerful reporting interface for detailed historical analysis Reports include: •Production analysis •Pareto analysis •Energy consumption •Faults and alarms Custom analysis: •Machine tool failure and crash analysis •Production gaps and auto- classification •Energy usage patterns Powered by Tableau Server, the most advanced business intelligence tool for reporting and analysis Thursday, October 4, 12
19. © System Insights, 2012 system insights Big Data Big Gap: Data Collection Thursday, October 4, 12
20. © System Insights, 2012 system insights Big Gap: Collection Lack of standardized data sources in the shopfloor: complex, disparate types of equipment with proprietary interfaces Biggest gap in enabling end-to-end connectivity and analysis standards? Thursday, October 4, 12
21. © System Insights, 2012 system insights MTConnect •Open-Source data exchange standard •Based on open protocols •Extensible and lightweight What is MTConnect? • Incompatibilities in protocols • Incompatibilities in data formats • Hard to configure/set up • Proprietary silos of technology Addressing Problems Open standard defining protocol and interchange format to facilitate communication between devices in manufacturing systems •Uses standardized tags in schema •Lightweight: can act as enabler for other standards •Unintrusive: Does not assume any business logic •Simple: Enables connectivity in legacy equipment MTConnect and Other Standards Thursday, October 4, 12
22. © System Insights, 2012 system insights An Integrated Solution Java SE Embedded powered devices for data collection across the shopfloor Device-agnostic integration with CNCs, PLCs, Sensors, Power Meters, and other data sources Leverage MTConnect open standard for data interoperability Realtime and historical analysis using vimana cloud-based platform for big data Manufacturing Shop Floor System Insights vimana Cloud Device 2 Device 1 Java SE Embedded Technology Device ... Device n Analysis Engine Device- Specific Adapters MTConnect Agent MTConnect Thursday, October 4, 12
23. © System Insights, 2012 system insights Requirements Pervasive: Ubiquitous, cost- effective Device Agnostic: Legacy, modern, sensor systems Standards-based: MTConnect, OPC, ... Scalable: Small shop to the Enterprise Java SE Embedded Technology Analysis Engine Device- Specific Adapters MTConnect Agent Thursday, October 4, 12
24. system insights © System Insights, 2012 Athulan Vijayaraghavan athulan@systeminsights.com Thursday, October 4, 12