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The Internet of Things (IoT) and Analytics

The Internet of Things (IoT) and Analytics. Class 4: Examples of Big Data Analysis. March 10, 2016 Louis W. Giokas. This Week’s Agenda. Monday The Different Things of the IoT Tuesday A Look at Communications and Devices Wednesday Cloud Storage and Formats in the IoT

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The Internet of Things (IoT) and Analytics

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  1. The Internet of Things (IoT) and Analytics Class 4: Examples of Big Data Analysis March 10, 2016Louis W. Giokas

  2. This Week’s Agenda Monday The Different Things of the IoT Tuesday A Look at Communications and Devices Wednesday Cloud Storage and Formats in the IoT Thursday Examples of Big Data Analysis Friday Machine Learning & Analysis Techniques

  3. Course Description • The IoT generates a vast amount of data. • This data can be used for many purposes, from product design, service and support, marketing, and control. • There are three levels of devices: the things, communications infrastructure and storage. • Tying it all together are analytic techniques. • In this course, we will build from the bottom up and then look at how the analytics infrastructure can be used in applications.

  4. Today’s Agenda • IoT Analytics Benefits • Use Cases • Platforms

  5. IoT Analytics Benefits • Business Transformation • Efficiency and savings • Internal analysis of operations • Growth opportunities • Plant utilization • Supply chain efficiencies • Market opportunities • New business models • New revenue streams

  6. IoT Analytics Benefits • Process automation • Remote monitoring • Visibility into asset health and maintenance • Responsive asset management • Predictive Maintenance/Asset Management • Industrial • Public infrastructure • Better budgeting • Responsive manufacturing • Automated planning

  7. IoT Analytics Benefits • Business Responsiveness • Respond to: • Competition • Supply chain changes • Customer demand • Market changes • Process change • In response to the above • Automate analysis

  8. Use Cases • Product development • Understand how existing, similar products are used • Track issues with current version • Actual product use data from your own data • Service calls • Collected data (devices self reporting) • Social media reaction • External factors • Combine the data to plan future versions and enhancements

  9. Use Cases • Product marketing • Detect industry trends • External factors • Weather • Competitors • Social media • What is “trending” • Current product performance

  10. Use Cases • Product Lifecycle Management (PLM) • A growing area of product development and design • Encompasses many of the previous use cases • Integration of many data sources with CAE, CIM and CAD systems • Drive product decision with data • Release schedules • Pricing

  11. Use Cases • Predictive maintenance • Find trends and predict failure times • Proactive vs. reactive • Schedule maintenance and upgrades • Merge IoT data with other schedule information • Customer requirements • Software upgrades • Many products with embedded processors can be made more efficient with a software change • Simulate to predict improvements • Test against real data.

  12. Use CasesPredictive Maintenance • Benefits • Identifies key prediction factors • Determines likelihood of predicted outcomes • Optimizes decision making • Systematically apply institutional knowledge • Extending asset life • Uncover root causes • Determine optimum correction actions • Enhance diagnostic capabilities

  13. Use CasesPredictive Maintenance • Data Dimensions • Structured • Industrial control systems (e.g., SCADA) • ERP • CRM • Financial • Unstructured • E-mails • Operator logs • Social media • Streaming • PLCs • Telemetry • Weather

  14. Use CasesPredictive Maintenance • Analytic Techniques Used • Data Mining • Anomaly Detection • Clustering • Classification • Regression • Text Mining • Machine Learning • Learn from the data • Simulation

  15. Platforms • Many automation vendors are offering platforms and solutions • General Electric • Siemens • Software vendors are also creating platforms for IoT analytics, integrating the various data sources • Ansys • IBM

  16. PlatformsGE: Predix.io

  17. PlatformsGE: Predix.io • Framework for developing Industrial IoT Analytic applications • Industry specific packages • Brilliant Factory • Digital Power Plant • Many more… • Lots of partners

  18. PlatformsSiemens PLM • A set of software technologies geared toward product design and development • Centered around Product Data Management (PDM) • Other components include • CAD • CAM • CAE (including simulation) • FEA • MOM (Manufacturing Operations Management) • Testing • Digital manufacturing

  19. PlatformsMicrosoft • Software based, general purpose analytics infrastructure for IoT Analytics • Brings together existing software products • Cloud based (Azure) • This is a toolkit with specific analytic tools targeted to the IoT • Azure HDInsight • Azure Machine Learning • Azure Data Factory

  20. PlatformsMicrosoft

  21. PlatformsIBM • Another software platform utilizing existing tools with IoT specific applications and architecture • IBM Bluemix cloud platform, or other cloud platforms • Watson for deep learning analytics

  22. Platforms IBM

  23. Summary and Preview • Today we have discussed three aspects of IoT Analytics • Benefits • Some use cases • Platforms • Tomorrow we will look at: • Machine Learning • Analysis Techniques • Statistical Methods

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