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How komatsu is driving operational efficiencies using io t and machine learning 6.7.18

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In this joint webinar, Jason Knuth, data scientist and analytics lead at Komatsu shares how they are analyzing over 17 billion data points every day from connected devices and using machine learning and analytics to improve mining operations.

Publié dans : Technologie
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How komatsu is driving operational efficiencies using io t and machine learning 6.7.18

  2. 2. Your Speakers Today… Vijay Raja Solutions Marketing Lead, IoT Jason Knuth Product Manager, Analytics and Smart Solutions
  3. 3. IoT Data Characteristics - Why this is a Big Data andAnalytics Problem? IoT data comes from a variety of different sources • Massive volumes of intermittent data streams • Generated from a variety of data sources • Predominantly time-series • Can come in streams (real-time) or batches • Diverse data structures and schemas • Some of it may be perishable Combining sensor data with contextual data is the key to value creation from IoT
  4. 4. 4 © Cloudera, Inc. All rights reserved. CLOUDERA ENTERPRISE The modern platform for machine learning and analytics optimized for the cloud Amazon S3 Microsoft ADLS HDFS KUDU SECURITY GOVERNANCE WORKLOAD MANAGEMENT INGEST & REPLICATION DATA CATALOG Core Services Storage Services ANALYTIC DATABASE DATA SCIENC E EXTENSIBLE SERVICES OPERATIONAL DATABASE DATA ENGINEERING
  5. 5. Cloudera Enterprise – The Data Mgmt. Platform for IoT Connected Devices/ IoT Data Sources Internal Systems External Sources BI Solutions Real-Time Apps Search Data Science Workbench SQL Machine Learning Data Center Hybrid Cloud Sensor/ IoT Data • Data Storage • Data Processing • Machine Learning • Real-time Analytics OPERATIONS Cloudera Manager Cloudera Director DATA MANAGEMENT Cloudera Navigator Encrypt and KeyTrustee Optimizer BATCH Sqoop REAL-TIME Kafka, Flume PROCESS, ANALYZE, SERVE UNIFIED SERVICES RESOURCE MANAGEMENT YARN SECURITY Sentry, RecordService FILESYSTEM HDFS RELATIONAL Kudu NoSQL HBase STORE INTEGRATE BATCH Spark, Hive, Pig MapReduce STREAM Spark SQL Impala SEARCH Solr SDK Partners Other Enterprise Data Sources
  6. 6. Powering a Variety of IoT Use Cases… Connected Vehicles Usage Based Insurance Industrial IoT Smart Cities & Ports Energy & Utilities Smart HealthcarePredictive Maintenance Aerospace & Aviation
  7. 7. 7 Jason Knuth Product Manager - Analytics & Smart Solutions Komatsu @Dataguy21 The Digital Mine How data and analytics are optimizing mineral extraction
  8. 8. 8 Video 视频 Customer Value
  9. 9. 9 Company overview Smart Solution Centers • Around the clock monitoring • Custom Data Solutions • Productivity analysis • Training • And more…
  10. 10. 10 Mining market challenges Mines are driven by cost per ton Today’s challenges: • Increasing social and regulatory issues • Difficult mining conditions – deeper ore deposits • Declining commodity prices • Inventory management • Aging workforce
  11. 11. 11 New challenges need new solutions Move from equipment-centric to holistic solutions - Smart equipment with built-in sensors - Advanced services beyond field repairs - Data monitoring to manage machine health - Optimize mine performance - Lowest cost per ton Monitoring Continuous Improvement(CI) Repair &Maintenance (R&M) Productivity (ton/h) Production Cost ($/ton) TCO ($/h)
  12. 12. 12 Smart Solutions Definition Smart Solutions are integrations of our smart connected products and systems, advanced analytics and direct services customized to solve customers’ toughest challenges. • Current offering of smart connected products: Shovels, Wheel loaders, LHD, Drills, Draglines, Trucks, Longwall systems (shearer, AFC, PRS, Pump stations) Conveyors, Continuous Miners, Roof Bolters, Feeder breakers, FCT, Shuttle car, Battery Hauler • Technology products: Controls and drives, environmental, Mining intelligence, Operator assist, Proximity detection• 24/7 support in over 20 countries • Application engineers experts • Machine assembly and rebuilds • Life Cycle Management • Component Exchange Program • Customized training • Inventory management • Service products and consumables • Technical and field services • Prognostics and health monitoring • Sub-second data • Komatsu analytics • Operational Excellence • Service Excellence Smart Solutions
  13. 13. 13 Data System Functionality
  14. 14. 14 Our IoT Analytics Journey Future Opportunities • Cloudera PaaS - Altus • End-to-end data governance • End-to-end data security • Edge analytics Gen 1 Gen 2 Gen 3 Gen 4 • Limited analytical capabilities • Data processing and storage costs • Limited machine learning capabilities • Data silos Increasing value and business impacts Historian Based Out-of-the-Box Proprietary Solution Cloud based Open Architecture & Platform Driving Intelligence at the Edge + PaaS • Limited flexibility/ interoperability • Scaling to data growth was a challenge • Data processing and storage costs • Challenges around custom use case Current State Future State • Cloudera – Cloud ecosystem • Processing, analytics and machine learning for IoT data • Processing 200,000 data points/second • Rapid prototyping 3-2 Years Ago9-3 Years Ago
  15. 15. 15 Managing the data This machine: 1,250 sensor points 550 alarms & events All machines: 17 billion time series data points with 3xPeak 6 million Alarms & events 150k analytics executed Each day
  16. 16. 16 Analytics Platform
  17. 17. 17 Data insights
  18. 18. 18 Data value chain Decision Making • Reporting & dashboarding • On-time decision making System Optimization • Real-time asset monitoring • Service lifecycle management • Lowest cost per ton Continuous Improvement • Support optimization • Product use enhancement Sales & Marketing • Market intelligence • Sales forecasting Improved Logistics • Supply chain optimization • Production forecasting Product Development • New product development • Product enhancements • Product resolution Mine learnings ● Essential partnerships ● Solutions aligned to customer goals ● Systems approach Enhanced equipment● Customized solutions ● Optimal machine availability ● Integrated parts and service support
  19. 19. 19 Analytics Example--Problem Identification Mineral extraction is rapidly becoming more challenging - increasing the duty cycle of the traction gearbox Our onsite service teams noticed an increase in gearbox anomalies Dynamic Steady Temperature
  20. 20. 20 Analytics Example-- Modeling and Evaluation As OEM, leverage engineering-based modeling to create new feature Each machine is unique signature, digital fingerprint Engineering has since redesigned the gearbox for the new conditions closing the data value chain. The 3rd generation platform enables analytic development from months  days Irregular Normal 3-5 Days 7 Days 1 Day
  21. 21. 22 The digital mine Focused on optimizing mineral extraction for the least impact - Safety - Autonomy, increased reliability - Environment - Less waste, improved efficiency - Minimal footprint - Productivity - Continuous improvement - Bringing lean practices to mining - Maintenance - Less wear and tear on the machines - Reducing unplanned downtime - Reducing maintenance time
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