Hadoop Summit 2016 Preventive maintenance of Robots
in Automotive industry
Ari Flink, Amit Kumar
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• 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
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Ari fun fact: Kemi, Finland
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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
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What I love doing: Bikram Yoga @ 105 F
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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
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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
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Audience?
• Big Data ?• Robotics / car manufacturer?• IoT ?
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IoT evolution
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The Four Eras of Compute
1960 1980 2005 2015
Mainframe
x86 Linux Web VMs iPhone
PC + Web Cloud + MobileIoT + Analytics + Automation (ML)
Cloud Containers
Enterprise Consumer IoT ( Machines )
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Why preventative maintenance for robots
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How much does unplanned downtime cost a car manufacturer?
$20k per minute
How much can a single incident can cost?
$2 million
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Million dollar question
Which robot will fail next?
How can we predict robot failure?
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Keep the assembly line moving
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Why does a single robot failure matter?
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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
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Platform for Preventative Maintenance
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Overview
$2 million outage avoided !
Telemetrycollected
Notify robot manufacturer
and plant
PlantDataCollector
Cisco Cloud
Partswarehouse
Car plant
Scheduledmaintenance
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Cisco Cloud
Automotive manufacturer APlant 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 BPlant 2
Plant 1
ZDT Data Collector
Cisco IoT Platform
Plant 3
Notifications
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Cisco Cloud
Car PlantCar Plant
Batch Layer
Cisco Cloud: High Level Arch Framework
Speed Layer
Serving LayerMasterdataset Batch
view Batch view
Real-time view
App
Car Plants
Batchprocessing
Real-timeview
Real-timeprocessing
DataIngestLayer
Datastream
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Cisco Cloud
Batch
Case study: ZDT Cisco Cloud Pilot details
Real-time
Serving
Masterdata
Computeddata
HBase
Ingest
Cisco IoT
Kafka
Flume
Spark Streaming
Batch processing:Pig, Hive
Impala
Impa
la S
QL
schema
Data
API
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UI
Hadoop
Multi-tenancy
User Interfaces
API
SQL(Impala)
HDFS
Customer Portals
Mobile Devices PD BI
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Next Gen Platform: “Predictive” Maintenance
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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”
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Sweet Spot: “Not-too-early” and “not-too-late” either
Time
Met
ric
Sweet spot
Too early Too late
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Preventative Maintenance
Unscheduled outage avoided
Torque out of range
Notify robot manufacturer
and plant
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Predictive analytics: Increased ROI
Repor
t• What
happened
Analyze
• Why did it happen
Monitor
• What is happening now
Predict• What
might happen
Increasing ROI and Complexity
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Data Modeling details
Initial Dataset
Run/Evaluate Models
Gather Data
Define Problem
Validation Dataset
Test Model
Select Model
Test Dataset
Apply Model Run Prediction
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Stream Processing Layer
HDFS
Data Ingest Layer
Predictive Analytics: High level architecture
Learning Layer
Action Layer
Rawdataset
Processeddataset
KafkaCisco
IoTPlatform
Near “real-time” (micro-batch) processing
( Spark )
Machine Learning ( Spark ML )
HDFSKnowledge
Base
Operational Dashboard platform
( custom built / Sensu
customized )
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Stream Processing Layer
HDFS
Data Ingest Layer
Predictive Analytics: High level architecture
Learning Layer
Action Layer
Rawdataset
Processeddataset
Near “real-time” (micro-batch) processing
( Spark )
Machine Learning ( Spark ML )
HDFS
Knowledge Base
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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
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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)
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Stream Processing LayerData Ingest Layer
Predictive Analytics: High level architecture
Learning Layer
Action Layer
Operational Dashboard platform
( custom built / Sensu
customized )
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Action Layer
Predictive Analytics: Action layer
Event store
Event consumer API based event Topic
Consumer for email
Events from the
Predictive platform
Dashboard middle-tier
API for Ad-hoc queries
Consumer for PagerDuty
Custom built / Sensu
customized
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Recap
Unscheduled outage avoided: Savings $40 million
PRESENTATIONEND
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