Advanced Infotronics Technologies for Smart Life Cycle Support

Transcription

1 Simulation-based Acquisition/ Advanced Engineering Environment Conference Advanced Infotronics Technologies for Smart Life Cycle Support A Closed-Loop Acquisition Jay Lee Wisconsin Distinguished and Rockwell Automation Professor and Director of Center for Intelligent Maintenance Systems (IMS) A National Science Foundation (NSF) Industry/University Cooperative Research Center (I/UCRC) 1

2 Trends in Maintenance Today Tomorrow Electronics Remote e Digital Transaction Internet- Medium GPM Assisted On Site Low Individual GPM Skills Ref: IBM 2

3 Competitive Intelligence Focus on Affordability, Survivability, and Sustainability Design Focus on Velocity Manufacturing and Supply Systems Focus on Smart Service & Innovative Life Cycle Support. Products After Market Core Intelligence Predictability Producibility Productivity Pollution Prevention Performance 3

4 Evolution in Product, Manufacturing, and Quality Product Focus 198 Intelligent Mechatronics (data & control intelligence) Product That Thinks and Links (information & computer intelligence) 21 Products That Learn, Grow, Reconfigure, and Sustain (knowledge & distributed Intelligence) Manufacturing Focus Factory Automation (flexibility) Enterprise Integration (agility) E-Logistics & E-Manufacturing (velocity ) Quality Focus SPC, TQM, TPM for Manufacturing Process (factory) Six-Sigma For Business Process (enterprise) Near-Zero-Downtime & Sustainability and Asset Optimization (customers) IMS Center Jay Lee 4

5 The Business Model Demand Inventory Capacity Predictive Intelligence to Informate Decision Execution Demand Inventory Capacity Execution 5

6 Infotronics Technologies? Infotronics Technologies intertwine advanced information and electronics systems intelligence and enable autonomous business functions and objectives through the use of internet and other tether-free technologies (i.e. wireless, web, ) IMS Center 6

7 Current Industry Maintenance Practices Reactive >5-6 % Preventative >4-5 % Predictive <5% Fail & Fix Routine maintenance Fix before Fail Cost of Maintenance 1x 6% 5x of all planned maintenance is unnecessary. ARC Advisory Group Predictive Preventive Reactive 7

8 Vision Enhanced Six-Sigma Design Design for Reliability And Serviceability Acquisition Product Redesign Robust Design Product or System In Use Monitoring Sensors & Events Degradation Watchdog Agent Self Maintenance Redundancy Active Passive Communications Tether-Free (Bluetooth) Internet TCP/IP Web-enabled D2B Platform Predictive Performance Web-enabled monitoring, Prognostics, and diagnostics Agent for Data Mining Logistics Integration Tools Asset Optimization Knowledge Mgt. 8

9 IMS Core Technologies Watchdog Agent for Predictive Prognostics Web-enabled Smart D2B (device to business) Platform and Tools for Data Transformation, Optimization, and Synchronization Applied Wireless Technologies 4. Logistics Infotronics Agent (LIA) 9

10 Need for Prognostics Degradation Process? Signal Processing Need On-Site Experts Diagnostics Sensors?? MODEL Monitoring Watchdog Action Prognostics Assessment Remote Experts 1

11 Watchdog Agent 11

12 Correlation of multiple signals into Performance Prediction- CV Sensor signals Time & Frequency Domain Time-Frequency Moments Confidence Value- CV Mahalanobis Distances Principal Components 12

13 Performance Index Model Based Prognostic Approach Step 1: Performance index modeling Using history data: Normal data Failure data Probability of failure: Pr{ 1 x v r } = f ( ) 1 x Using parameters fixed after each maintenance: Beginning setting Confidence: r r C ( x) = f 2( x) Acceptable level Step 2: Current performance calculation M Current inputs x r Pr{ 1 x r } C(x v ) Failure probability Confidence Step3: Degradation tendency prediction Determine model structure of probability or confidence curve y( k) = φ y( k 1) + L+ φ y( k p) 1 p + ε( k) θ ε( k 1) L θ ε( k q) 1 q With Box-Jenkins method On-line parameters (,φ i ) θ i identification, then predict Failure Line Predict failure probability or confidence continuously 13

14 Mean Time Between Degradation 1 CV Degradation threshold time Mean Time Between Degradation MTBD = t P ( t ) dt time Trigger point Predicted degradation 14

15 Example Max speed/mps maintenance Time/days 4 Cycle Time/s maintenance Time/days Probability of failure 1.5 Failure Line point1 point2 maintenance prediction2 prediction Time/days 15

16 Hybrid Prognostics and Diagnostics Virtual Instruments Hashing Product Nervous Systems Degradation (Prognostics) Performance Measure when Performance Status When degradation occur Sensors Look up tables signal for degradation Production System/ Machine Fusion Neural Network Genetic Algorithm Statistic Computing Fuzzy Logic.. Fusion Diagnostics Diagnostics + Prognostics Internet where what why Identify reasons 16

17 Similarity Matrix for Performance Comparison MACHINE 2 A B C D E F G H A -- MACHINE 1 B C D E F G H

18 Simulation of Throughput Response Surface Between Parameters Sensitivity Sensitivity Degradation of (X1 & X2) 18

19 Watchdog Toolbox CMAC Learning Method Watchdog Toolbox Joint T-F Method Hybrid Techniques Applied Statistical Methods 19

20 Embedded Monitoring Prediction & Performance Assessment Data/Information Transformation D2B Platform XML Web Optimization Synchronization e-business, e-manufacturing, e-service Automation Optimization of Production, Delivery, Maintenance, Human Resources, Logistics Systems etc. DATA/INFORMATION Device/Factory Level Transformation into KNOWLEDGE Real Time DECISION/ Synchronization 2

21 Applied Wireless Issues Communication Network Wireline vs. Wireless Performance - Throughput vs. Latency Reliability - Control vs. Monitoring Cost - ISM Band Wireless Technologies IEEE Bluetooth (IEEE 82.15) Proprietary STA AP Building Layout - Sheet Rock Walls d Attenuation (db) Building Layout - Cinder Block Walls Attenuation (db) Radio Propagation Issue Prof. Ivan Howitt, EE UWM 21

22 Tether-Free Embedded Infotronics Agent HiDRA HiDRA from Rockwell Scientifics 22

23 Remote Monitoring and Prognostics of Defenses Equipment For short distance transmission: Bluetooth - etc. Cellular phones or GPS for long range transmission Alarms Prognostics Remote diagnostics Service Dispatching Synchronization 23

24 Logistics Infotronics Agent (LIA) 1. Initial product info is written in the product embedded device Usage, Service & Maintenance Product delivery 5. Design Innovation through life cycle intelligence Logistics Infotronics Agent (LIA) 2. Wireless bluetooth connection between mobile device and product embedded device Tether-Free Transmission 4. Update product usage information in the Life Cycle Monitoring System 3. Wireless Internet connection between mobile Internet device and producer s product life cycle system Producer s Product Life Cycle Intelligence (LCI) System 24

25 Logistics Infotronics Agent (LIA) 25

26 Customer Needs Product, Process, and Enterprise Design (PPED) e-procurement Enterprise Database Mgt System (EDMS) (design, manufacturing, suppliers, testing, etc) Today s Acquisition and Logistics Systems Supplier Evaluation & Selection Life Cycle Service & Maintenance (LCSM) Decision Manufacturing Enterprise Execution System (MEES) Final Product Assembly and Testing Supplier 1 e-shops Supplier 2 e-shops. Supplier N e-shops Sourcing 26

27 Customer Needs Design for Six-Sigma (DFSS) Product, Process, and Enterprise Design (PPED) e-procurement Prediction, Validation & Optimization Enterprise Database Mgt System (EDMS) (design, manufacturing, suppliers, testing, etc) Logistics Infotronics Agent (LIA) Supplier Quality Supplier Evaluation & Selection Life Cycle Service & Maintenance (LCSM) Tether-free Interface Tether-free Interface Data Accessibility & Monitoring Manufacturing Enterprise Execution System (MEES) Decision Final Product Assembly and Testing Supplier 1 e-shops Supplier 2 e-shops. Supplier n e-shops Sourcing 27

28 Current Members and Sponsors Rockwell Automation Ford Motor GM Johnson Controls Eaton Harley-Davidson Xerox Wisconsin Electric Power United Technologies Siebel Systems GE Medical Systems Toshiba (Japan) A.O. Smith Intel U.S. Postal Services National Instruments DaimlerChrysler ITRI Lantronix PMC Micro Mobio Wireless Kone Elevator Cognex API Boeing Genex Technologies DoD DMSO ATOP Motorola Dr. Machine.Com ABB Hitachi Seikei Caterpillar Velicon SEMATECH Eagle Technologies Industrial Objects InterNext Group Citation Custom Products 28

29 Value through Leveraged Partnerships Company Members Testbeds and and Projects Knowledge, Best Practices, Tools, and Students Membership and Partnerships IMS Methodologies and Enabling Technologies 29

30 Why IMS? Fail and Fix or Fly and Fix FaF IMS Methodologies Predict and Prevent PaP Risk Reduction, Technology Readiness, and New Breed of Engineers and Workforce IMS Testbeds (e-maintenance/ e-manufacturing/ e-logistics Integration) Partnerships 3

31 Thank You For More Information 31