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

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

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.

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.

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

  1. 1. DRIVING OPERATIONAL EFFICIENCIES USING IOT & MACHINE LEARNING WEBINAR
  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
  22. 22. 23

Notas do Editor

  • GM – welcome to the session on IOT
    Vijay Raja – Solutions Marketing Lead for IoT
    Dave Shuman –

    In the next 50 minutes we’ll try and dive into
    How Cloudera fits in the IoT Ecosystem
    What are some of the key enabling capabilities specifically around IOT
    How we are working with partners to drive an end-to-end architectures for IOT
    More importantly, we will talk about use cases
    We’ll talk about what Customers are doing with Cloudera on IoT today
    We will walk through a number of customer case studies and examples
    Hopefully we will have some time towards the end for some Q&A as well…
  • IoT will generate the volume and variety of data greatly exceeding those with which information leaders are familiar with today, requiring modernization of information infrastructure to realize value.
    Some of the key characteristics of IoT data include:

    Massive volumes as we saw in the previous slide
    But it is not just about the volumes that organizations need to be prepared for
    We are talking about a wide variety of data sources, structures, schemas and formats – from sensor readings of temperature and pressure at one end to doing video analytics on live video streams
    Most importantly it may come in streams (real-time) for lot of the IOT use cases or in batches – You need a data management platform that can manage both data-in motion and data at rest

    Most importantly, the real value from IoT comes when you can combine that sensor data with other internal and external data sources – adding context to that sensor data
  • Let’s take a look at how Cloudera’s EDH fits into the IoT Ecosystem

    Can ingest data from multiple sources including real-time streaming sensor data
    You can combine the sensor data with data other internal and external sources to drive business insights
    You can deploy EDH on prem (in your data center) or on hybrid cloud environments and still be able to manage it centrally
    And you can serve and analyze the data in a number of different ways - integrate it with existing BI solutions, do SQL queries with impala, or do machine learning or integrate it with real time applications (such as OnCommnad Connect in the case of Navistar)
  • Our equipment and service offerings cover a full range of mining needs and can be combined to provide full-system solutions for surface and underground mining. Integrated services and solutions help customers
  • Serve customers for the life of the machine
  • MarComm

    Read the definition
    Dissect the graphic:
    “We realize the definition is fairly long and cumbersome, so the intention of this graphic is to simplify the understanding of what JoySmart Solutions are. At Joy Global, we have been offering smart products, direct services and analytic services for a while independently. This new approach, called JoySmart Solutions has the intention of integrating these 3 main offerings with the objective of delivering value to the customer. The key aspect here that differs from everything else we have been doing until today, is that we are partnering with our customers to understand their challenges, and will offer them an integrated solution of smart products, direct service and analytics that will drive to an specific result, which will move the needle. Hence the colored dashboard on the center of this graphic. JoySmart Solutions are the means to an end result.”
  • Onsite team contacted our Smart Solution Analytics teams to pull data prior to the anomaly to determine if they could detect the event prior to need for manual intervention
    Through data preparation multiple machine sensors were identified as possible predictors, additionally from our domain knowledge of our equipment we created additional features based on engineering principles.
  • We Applied the Principle Component Analysis (PCA) to the 6 traction gearbox Sensor data.
    We utilized a random sample of 50% of the data from the first 40days assuming this will represent “normal” operation
    Calculated the principle components and their coefficients to be used to score the full data set
    After further evaluations of the axis coefficient’s it was determined that 2 of the engineered features were the best at describing the irregularity in the gearbox. In the end we utilize a Gaussian Mixture Model of the 2 features to describe the machine's digital finger print. Since conditions can vary from customer to customer we apply a digital finger print to each machines. Live data coming from the machine is evaluated against its own model.
    Our engineering team evaluated these models and conditions to determine the best methods to redesign the gearbox for the customers tougher conditions. Since then these gearboxes have been deploy to tackle these new conditions. This is a good example of how we close the data value chain that I spoke about before this example.
    Our 3rd generation platform enables us to develop analytics and models like this example in days rather than months when comparing to our previous platforms. The key driver in the improvement was breaking down the data silos and enabling quicker data wrangling and feature creation.
    We are excited to see the rapid development of the IoT space with new technologies to enable us to deliver these types of analysis to our customers. Here is a video highlighting the path forward for Komatsu.
  • There’s no out-of-the-box solution for mining, but every solution we create in today’s digital mining environment focuses on …

    Data is critical part of our journey to a more sustainable, less impactful mining environment
    Still allow us to produce the essential minerals needed for modern society in a more efficient way

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