The Internet of Things and Factory of Future

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2 The Internet of Things and Factory of Future Andy Chang Sr. Manager, Academic Research & Industrial Internet of Things 2

3 Our Mission We equip engineers and scientists with systems that accelerate productivity, innovation, and discovery. 3

4 Our Customers Advanced Manufacturing Industry Research Consumer Electronics Wireless Energy Transportation 4

5 5 The Escalating Complexity of Systems... control systems actuators electrical systems sensors control systems actuators electrical systems sensors control systems actuators electrical systems sensors camera dynamic models sensor fusion wiring algorithm design motion control payload simulation state space control filtering kinematics steering control systems actuators electrical systems sensors camera sonar dynamic models sensor fusion wiring algorithm design perception motion control payload simulation vision processing state space control filtering adaptive control mapping UI kinematics steering control systems actuators electrical systems sensors camera sonar phone dynamic models sensor fusion wiring multicore processing algorithm design perception motion control payload simulation vision processing state space control filtering adaptive control mapping localization UI kinematics steering FPGA actuators electrical systems sensors control systems camera sonar phone dynamic models sensor fusion wiring multicore processing algorithm design perception motion control payload simulation vision processing physics simulation

6 Big Analog Data Integration of Computation, Communication, and Control Cyber- Physical Systems Internet of Things Big Analog Data: High-Volume Data Cyber-Physical Systems: Control Systems Internet of Things: Network Connectedness 6

7 Enabling Laws for Internet of Things Moore s Law P n = P o 2 n Metcalfe s Law C m = m(m 1) 2 7

8 Perspective Pope Election

9 Perspective Pope Election

10 IOT WILL IMPACT 75% ENGINEERS IN 3 YEARS - VDC 50 BILLION DEVICES CONNECTED BY X INCREASE IN THINGS - GARTNER, BILLION SMART PHONES 17.5% CAGR - IDC, 2014 $19 TRILLION OPPORTUNITY 10

11 Landscape of Internet of Things Business Analytics Internet of Things BIG ANALOG DATA INTERACT COMPUTE CONNECT BIG DATA Internet of Services Social Web 11

12 Simple Schematic Digital Control Systems, ca, 1960 Setpoint Error Signal C(s) Control Effort G(s) Output Controller Plant Feedback Signal H(s) Sensor 12

13 Simple Schematic Computational (input) Network Fabric Computational (output) Feedback Signal Physical Plant Control Effort 13

14 Smart Edge Node Architecture The Physical World Analog Input Analog Output Digital Input? Computation FPGA CPU Data Connectivity Digital Output Infinite Data Any Bus Open Data 14

15 Smart Edge Node Architecture The Physical World Interact Compute Connect Data Connectivity Infinite Data Any Bus Open Data 15

16 Control Methodology Bare Iron Level 16

17 Control Implementation Tools Targets Math (.m file script) Host Control (C, C++,.NET) Simulation (Hybrid) DSP (Fxd pt C, Assembly) User Interface (HTML) FPGA (VHDL, Verilog) H/W Driver (C, Assembly) System Debug FPGAs Multicore Processors (Desktop & RTOS) Levels of abstraction for concurrent algorithms Parallel processing, multi-tasking increases system complexity Model of computation 17

18 Algorithm Engineering for Control Systems Towards Lab-Based MOOCs, Seshia, UC Berkeley,

19 System Design on Timing Computational (input) Network Fabric Computational (output) Feedback Signal Physical Plant Control Effort Need Time-Sensitive Network!! 19

20 Status Quo in System Design (V Model): There are several areas where change is necessary Planning Constrain: N/A Optimize: Cost Programming Constrain: Performance Optimize: Cost Budgeting Constrain: Cost Optimize: N/A Execution Source Selection MILD Systems Engineering Verification & Validation MIL STD 499A (1969) systems engineering process: as employed today Design process arbitrarily decomposes system and largely ignores complexity undesired and multi-mode interactions and emergent system behaviors Cost-centric acquisition process provides dis-incentives to incorporation of flexibility and adaptability into system designs Cost Optimization SWaP Optimization SWaP Optimization System Functional Specification Power Data & Control Thermal Mgmt Constrain: Performance Optimize: Cost Constrain: Performance Optimize: SWaP System Layout Subsystem Design Component Design Constrain: Performance Optimize: N/A Integration Component Testing Verification & Validation Subsystem Testing Conventional V&V techniques do not scale to highly complex or adaptable systems (i.e., those with large or infinite numbers of possible states/ configurations) SWaP = Size, Weight, and Power V&V = Verification & Validation Desirable interactions (data, power, forces & torques) Undesirable interactions (thermal, vibrations, EMI) 20

21 Platform-Based Design for Internet of Things Application Space Architectural Space Application Instance Platform Instance Platform Mapping Platform Design-Space Export Platform: library of resources defining an abstraction layer with interfaces that allow legal connections Resources do contain virtual components i.e., placeholders that will be customized in the implementation phase to meet constraints Very important resources are interconnections and communication protocols A. Sangiovanni-Vincentelli, UC Berkeley. Defining Platform 21 Based Design. EEDesign, Feb 2002

22 Platform-Based Design Software Desktops, Laptops and Mobile Modular Data Acquisition Platform Modular Test Platform Modular Embedded Control Platform 22

23 Platform-Based Design 23

24 Industrie 4.0 First programmable logic controller, Modicon 084 (1969) Fourth Industrial Revolution First mechanical loom (1784) First Industrial Revolution Water and steam powered mechanical production First production line, Cincinnati slaughterhouses (1870) Second Industrial Revolution Electric power and mass production Third Industrial Revolution Electronics and IT automation Data Intensive Computers, Communications, Control Industrial Internet Smart Factories Cyber-physical Systems complexity End of 18th century Start of 20th century Start of 1970s Today time Source DKFI

25 Status Quo for Manufacturing PAST AS IS PAST AS IS Shopfloor are not designed to evolve easily Large amount of manual operations fully dependent on workers 25

26 400,000+ Hole Locations 1,000+ Tightening Tools Increase Quality Assurance 26

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28 Intelligent Devices Pattern Recognition Tool Tracking 28

29 To Local Server 29

30 To Local Server 30

31 Factory of the Future Edge Processing Big Analog Data Smart Measurement Industrial Robot Smart Instrumentation Mobile Manipulation Smart Inspection Smart Tool Smart Communications Enhanced Requirements for the IIoT Reliability Latency Security Upgradeability 31

32 Visual Servoing 32

33 Factory of the Future Smart Tools Intelligent Communication Industrial Robots Smart Inspection Mobile Manipulator 33

34 Factory of the Future Airbus Technology Store Vision Smart Filter Intelligent Input/Output Motion Industrial SOM sbrio Smart CompactRIO PXI Mobile Tools Communication Robots Inspection Manipulator IP and Algorithm Library Embedded Hardware 34

35 IoT: Generalized System Architecture Edge Node System of Systems IT Infrastructure The Edge On Premise Off Premise Embed local intelligent to sensors and actuators Connectivity, data aggregation, and system analytics Cloud analytics, management, and services Data Flow: Real-Time In Motion Early Life At Rest Archive 35

36 Example End-to-End Industrial IoT Solution Factory of the Future: Factory-Wide Online Monitoring and Control Sensors/Actuators Data Acquisition and Control Systems IT Infrastructure Smart Glasses RFID RGB Camera NI System on Module The Edge Switches Wired or Wireless Network & PLM Torque Sensor Smart Tools RFID Single-Board RIO LabVIEW on PC Plant Node Server Motor Drives PWM Manipulator RFID CompactRIO Motor Drives 36

37 Factory of the Future q Cyber-physical production systems Real-time information from design to production Smart components will communication with automated machines q Human-centered enhancement through Smart wearable devices Smart production robots Smart tools q Connected factory with high data volumes through safety standards 37

38 Business Analytics Internet of Things BIG ANALOG DATA INTERACT COMPUTE CONNECT BIG DATA Internet of Services Social Web 38

39 Vision 2025 Every object will be smart The ensemble is the function! Function determined by availability of sensing, actuation, connectivity, computation, storage and energy Collaborating to present unifying experiences or to fulfill common goals A humongous networked, distributed, adaptive, hierarchical, hybrid control problem 39

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