Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Preventative Maintenance of Robots in Automotive Industry

1,895 views

Published on

Preventative Maintenance of Robots in Automotive Industry

Published in: Technology
  • Be the first to comment

Preventative Maintenance of Robots in Automotive Industry

  1. 1. Hadoop Summit 2016 Preventive maintenance of Robots in Automotive industry Ari Flink, Amit Kumar
  2. 2. • Intro • IoT evolution, Big Data in IoT • Cisco Cloud Platform • Case Study • Preventive maintenance of Robots in Automotive industry • Adaptive, self-learning next-gen Predictive maintenance platform Agenda
  3. 3. Ari fun fact: Kemi, Finland
  4. 4. About Ari Solutions Architect at Cisco Cloud • Architect service deployments on Cisco’s cloud platform (BDaaS, DBaaS, BSS) Previously Operations Architect at WebEx, eBay, Excite@Home • Ensure operational readiness for complex distributed services • HA, DR,, config, deployment, monitoring, event correlation
  5. 5. What I love doing: Bikram Yoga @ 105 F
  6. 6. Amit: my other passion Big Data Architect at Cisco Cloud Platform & Services Group • Big Data Solutions for clients and infra needs using Hadoop, Cassandra • Analytics platform design • Data Center infra software abstraction : Firewall as a Service, Networking as a Service. Previously Symantec/Verisign, HCL-US, BoA • Distributed Systems design and implementation • Hadoop based solutions for large data sets
  7. 7. Cisco Cloud Platform Global platform deployed across Cisco and SP Partners API-driven, elastic experience for developers, based on open standards Cisco-architected and operated for rapid application development and deployment
  8. 8. Audience? • Big Data ? • Robotics / car manufacturer? • IoT ?
  9. 9. IoT evolution
  10. 10. The Four Eras of Compute 1960 1980 2005 2015 Mainframe x86 Linux Web VMs iPhone PC + Web Cloud + Mobile IoT + Analytics + Automation (ML) Cloud Containers Enterprise Consumer IoT ( Machines )
  11. 11. Why preventative maintenance for robots
  12. 12. How much does unplanned downtime cost a car manufacturer? $20k per minute How much can a single incident can cost? $2 million
  13. 13. Million dollar question Which robot will fail next? How can we predict robot failure?
  14. 14. Keep the assembly line moving
  15. 15. Why does a single robot failure matter?
  16. 16. Zero Downtime • Cisco and Fanuc have created a Zero Downtime Solution (ZDT) that analyzes data from robots to detect potential problems that could lead to a failure. • ZDT is currently used in production with over 6,000 robots at automotive plants globally. GM alone has deployed ZDT in 27 factories in 5 countries analyzing over 5,000 of robots • ZDT has successfully detected over 45 cases of potential failure across 26 production plants over the past year and saved already customers $40 million
  17. 17. Platform for Preventative Maintenance
  18. 18. Overview $2 million outage avoided ! Telemetry collected Notify robot manufacturer and plant Plant Data Collector Cisco Cloud Parts warehouse Car plant Scheduled maintenance
  19. 19. Cisco Cloud Automotive manufacturer A Plant 2 Plant 1 Plant Data Collector Case study: Data Flow Cisco IoT Platform Plant 3 Cisco IoT Platform Cisco BDaaS ZDT application Reporting Analytics Car manufacturers Robot manufacturer Automotive manufacturer B Plant 2 Plant 1 ZDT Data Collector Cisco IoT Platform Plant 3 Notifications
  20. 20. Cisco Cloud Car PlantCar Plant Batch Layer Cisco Cloud: High Level Arch Framework Speed Layer Serving Layer Master dataset Batch view Batch view Real-time view App Car Plants Batch processing Real-time view Real-time processing Data Ingest Layer Data stream
  21. 21. Cisco Cloud Batch Case study: ZDT Cisco Cloud Pilot details Real-time Serving Master data Computed data HBase Ingest Cisco IoT Kafka Flume Spark Streaming Batch processing: Pig, Hive Impala ImpalaSQL schema Data API
  22. 22. UI Hadoop Multi-tenancy User Interfaces API SQL (Impala) HDFS Customer Portals Mobile Devices PD BI
  23. 23. Next Gen Platform: “Predictive” Maintenance
  24. 24. Why Predictive?  Car Production facilities operate at high volume  Unexpected downtime creates considerable losses  There is a need to be informed of a potential robot, controller or process problem before unexpected downtime occurs  Early detection is key in the following scenarios  Mechanical failures  Process control failures  System issues: Controller  Maintenance reminders  Not-too-early and not-too-late detection is “key”  Too early is expensive in the long run  Too late is detrimental as well  Finding the sweet spot is key to the most “optimal solution”
  25. 25. Sweet Spot: “Not-too-early” and “not-too-late” either Time Metric Sweet spot Too early Too late
  26. 26. Preventative Maintenance Unscheduled outage avoided Torque out of range Notify robot manufacturer and plant
  27. 27. Predictive analytics: Increased ROI Report • What happened Analyze • Why did it happen Monitor • What is happening now Predict • What might happen Increasing ROI and Complexity
  28. 28. Data Modeling details Initial Dataset Run/Evaluate Models Gather Data Define Problem Validation Dataset Test Model Select Model Test Dataset Apply Model Run Prediction
  29. 29. Stream Processing Layer HDFS Data Ingest Layer Predictive Analytics: High level architecture Learning Layer Action Layer Raw dataset Processed dataset Kafka Cisco IoT Platform Near “real-time” (micro-batch) processing ( Spark ) Machine Learning ( Spark ML ) HDFS Knowledge Base Operational Dashboard platform ( custom built / Sensu customized )
  30. 30. Stream Processing Layer HDFS Data Ingest Layer Predictive Analytics: High level architecture Learning Layer Action Layer Raw dataset Processed dataset Near “real-time” (micro-batch) processing ( Spark ) Machine Learning ( Spark ML ) HDFS Knowledge Base
  31. 31. File Formats: Avro vs Parquet vs ORC  Avro is row-based storage format, optimized for scans of all fields in a row for each query  Parquet is column- based, best used when dataset has many columns and only a few columns are worked on  ORC is column- based as well
  32. 32. Spark based Predictive platform on Hadoop Data Integration ( Kafka, Sqoop, Flume ) Storage for any type of data Filesystem (HDFS) Online NoSQL (HBase) Workload Management ( YARN ) Machine Learning (Spark, Mahout) Stream Processing (Spark)
  33. 33. Stream Processing LayerData Ingest Layer Predictive Analytics: High level architecture Learning Layer Action Layer Operational Dashboard platform ( custom built / Sensu customized )
  34. 34. Action Layer Predictive Analytics: Action layer Event store Event consumer API based event Topic Consumer for email Dashboard middle-tier API for Ad-hoc queries Consumer for PagerDuty Custom built / Sensu customized
  35. 35. Recap Unscheduled outage avoided: Savings $40 million

×