Preventative Maintenance of Robots in Automotive Industry
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Preventative Maintenance of Robots in Automotive Industry
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Preventative Maintenance of Robots in Automotive Industry
- 1. Hadoop Summit 2016 Preventive maintenance of Robots in Automotive industry Ari Flink, Amit Kumar
- 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. Ari fun fact: Kemi, Finland
- 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. What I love doing: Bikram Yoga @ 105 F
- 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. 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. Audience? • Big Data ? • Robotics / car manufacturer? • IoT ?
- 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. Why preventative maintenance for robots
- 12. How much does unplanned downtime cost a car manufacturer? $20k per minute How much can a single incident can cost? $2 million
- 13. Million dollar question Which robot will fail next? How can we predict robot failure?
- 14. Keep the assembly line moving
- 15. Why does a single robot failure matter?
- 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
- 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. 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. 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. 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. UI Hadoop Multi-tenancy User Interfaces API SQL (Impala) HDFS Customer Portals Mobile Devices PD BI
- 23. Next Gen Platform: “Predictive” Maintenance
- 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. Sweet Spot: “Not-too-early” and “not-too-late” either Time Metric Sweet spot Too early Too late
- 26. Preventative Maintenance Unscheduled outage avoided Torque out of range Notify robot manufacturer and plant
- 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. 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. 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. 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. 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. 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. Stream Processing LayerData Ingest Layer Predictive Analytics: High level architecture Learning Layer Action Layer Operational Dashboard platform ( custom built / Sensu customized )
- 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. Recap Unscheduled outage avoided: Savings $40 million
Notes de l'éditeur
- FANUC provides robots with high reliability and uptime averaging 9+ years (80k hours) mean time between failures. But this was not enough, because they know that when a production line goes down unexpectedly it can cost a company as much as $20,000 per minute or and $2 million for a single incident. In a factory that’s kicking out one vehicle per minute, every minute of stalled production is hemorrhaging profits, labor expenses and more. Zero Downtime (zdt) Reacting to unexpected downtime can mean an extended period of time for: problem solving ordering and receiving parts that are needed scheduling a service person servicing the robot and/or system Production facilities are operating at high volume Single robot failure can stop the line Unexpected downtime creates considerable consequences Need to be informed of a potential robot, controller or process problem before unexpected downtime occurs
- ZDT is FANUC's new diagnostic tool that detects critical information about the robot's mechanical, maintenance and process/system health to alert customers about potential system or product issues. Data relevant for the maintenance issue is sent to the Cisco Cloud where the cloud analytics engine captures the “out of range” exceptions and predicts the maintenance need. Then, an alert is sent from the cloud application to FANUC service personnel and to the manufacturing customer about the need for replacement part. The part is then shipped to arrive at the factory in time for the next scheduled planned maintenance window. Anything on the robot controller that predicts or prevents robot downtime is part of ZDT early detection of impending Mechanical failures Process control failures System issues
- FANUC Current installation base: 250,000 robots 2.4 million CNCs 12.7 million servo motors Zero Downtime (zdt) Reacting to unexpected downtime can mean an extended period of time for: problem solving ordering and receiving parts that are needed scheduling a service person servicing the robot and/or system Production facilities are operating at high volume Single robot failure can stop the line Unexpected downtime creates considerable consequences Need to be informed of a potential robot, controller or process problem before unexpected downtime occurs
- PaaS: Develop fog applications. Our first IoT PaaS offering, called Cisco DSX, simplifies fog application development in several ways: ◦ Device abstraction: Fog applications need to communicate with many types of IoT devices. Creating a separate application for each vendor’s temperature sensor, for example, would be impractical. Cisco DSX saves application developers this effort by providing an abstracted view of IoT devices. ◦ Support for multiple development environments. IoT applications that deliver machine as a service (MaaS) are typically developed in various environments and programming languages. With Cisco DSX, fog nodes can support multiple development environments. ◦ Simplified management of fog applications. Managing a growing number of fog applications would also be impractical. Cisco DSX simplifies management and automates policy enforcement.
- SPG Sofwqare Platform Group
- Motion/Mechanical Health Examples: Motor and Reducer deterioration, gear backlash , excessive servo-off events, Dislocation prediction for delta robots, etc. Process Health Examples: Air regulator failure, canister high torque, ServoGun motor/drive deterioration, abnormal tip wear, gas flaw, wire spool low, part rate increase beyond, etc. System Health Examples: Memory utilization, Ethernet Transmit/receive errors, Vision performance, CPU performance issues, etc.