MINING 4.0. Professor Diego Galar Lulea University of technology

Transcription

1 MINING 4.0 Professor Diego Galar Lulea University of technology

2 The world is getting smarter more instrumented, interconnected, intelligent.

3 A smarter society is placing new demands on ICT. Pervasive instrumentation creates vast amounts of data & drive new types of applications that require real time data analysis and prediction. Advanced Search Financial Analytics Medical Imaging emaintenance 3D On-Line Infotainment Video Surveillance Analytics with Online Network Security

4 Therefore data flow

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6 The systemic approach of the asset Environment Enterprise System Inputs Labour force Load Grease Feedback Maintenance System Adjust Calibrate Inspect Repair Replace Outputs RUL Remaining Useful Life

7 A system of systems Environment Inputs Maintenance System Enterprise System Feedback Outputs Materials Labour Spares Tools Money Technology Information Facilities Capacity Quality Maintainability Availability Reliability Safety Cost

8 What to measure? SCADA VIBRATION TRIBOLOGY THERMOGRAPHY

9 Industry 4.0, the concept The basic principle of Industry 4.0 is that interconnected machines and systems will form an intelligent self-controlled network spanning the entire value chain. In the ideal factory, machines autonomously react to unexpected changes in production, predict failures and trigger maintenance processes. How does this Industry 4.0 dream of technology-enabled just-in time maintenance and near-zero downtime translate into reality?

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12 CMMS: A Data Repository

13 Language analysis in WO

14 Incidents - High Context tab screenshot

15 Language Processing

16 Unified data format: What connections are needed? Asset data Event data CM SCADA Content Layer Transaction Layer Asset Data Events CM SCADA Silos of data by functional area

17 A necessary merging process

18 Modern approach Traditional approach Modern approach

19 Decision layers for Mining 4.0

20 Different decision layers of data integration ERP - Financial data -Marketing data - Customer info - Suppliers - Regulations text st decision layer r maintenance terventions CONDITION MONITORING DATA PLANT INVENTORY/ ASSET INFO FUSION CM DATA / INTEGRATION WITH CMMS DATA MINING OF MAINTENANCE RELATED DATA SOURCES HISTORICAL DATA WORKORDER IN/OUT Macro level decision CMMS EAM No maintenance crew required DECISION INMEDIATE INTERVENTION OF ACTUATORS Micro level decision DECISION FOR TOP MANAGEMENT

21 Britagem II Peneiramento II CVRD - Carajás monitoring overview Rádio MASTER Rádio Slave 1 Modem Fibra Óptica Trans. Trans. Booster Moviscan 4 Transciever Modem Transciever Moviscan 1 Captação Rádio Slave 2 Modem Central Trans. Trans. BSM I Moviscan 7 Telefônica Moviscan 2 Recuperadora I Rádio Slave 3 Modem Trans. Trans. BSM II Moviscan 8 Moviscan 3 Recuperadora II Modem Modem Moviscan 5 Moviscan 9 Modem Britagem I Moviscan 6

22 New strategic services

23 Different decision layers of CMMS and CM integration ERP - Financial data -Marketing data - Customer info - Suppliers - Regulations text 2nd decision layer for top management CONDITION MONITORING DATA PLANT INVENTORY/ ASSET INFO FUSION CM DATA / INTEGRATION WITH CMMS DATA MINING OF MAINTENANCE RELATED DATA SOURCES HISTORICAL DATA WORKORDER IN/OUT Macro level decision CMMS EAM DECISION INMEDIATE INTERVENTION OF ACTUATORS Micro level decision Micro level decision DECISION FOR MANAGEMENT

24 The need for analytics and sensemaking Industrial Data and Mining data

25 Trend: Organizations are Getting Dumber All Digital Data Computing Power Growth Growing Dumber Sensemaking Algorithms Time

26 The Way Forward All Digital Data Computing Power Growth Context Engines Sensemaking Algorithms Time

27 What is context? Any information that can be used to characterize the situation of entities that are considered relevant to the interaction between a user and an application Dey et al. A pattern of behavior or relations among variables that are outside of the subjects of design manipulation and potentially affect user behavior and system performance Sato

28 Asset management and the context.hot topic mining 28

29 What is context awareness? An application s ability to adapt to changing circumstances and respond according to the context of use Issues in context awareness system implementing How is context represented? How frequently does context information have to be consulted? What are the minimal services an environment needs to provide to make context awareness feasible?...

30 ICT-enabled Intelligent Mining ICT is a key enabler for improving mining systems at three levels: Smart Mines Goal: More automation, better control & optimisation of factory processes Means: Software, lasers & intelligent devices embedded in machines & mine infrastructure Mine productivity Less waste Less energy use Faster time-to-market Better quality Sensors, Tags Product info data data info advice PLM agent (reader) PLM server Info request Virtual Mines Goal: To manage supply chains; to create value by integrating products & services Means: Software to holistically interconnect & manage distributed mining assets; new business models & value propositions Supply-chain productivity High-value products Keep jobs in Europe Process transparency IPR security Lower CO2 footprint Digital Mines Goal: To see the product before it is extracted Means: Software for the digital representation & test of products & processes prior to their use Design productivity Reduce design errors Better & efficient Less waste + rework Faster time-to-market -30-

31 Evolution of the Process Technological Advance Design & Validation of Manufacturing Processes 2D 3D Digital Mockup Knowledge Capture Digital Mining Integration of Product Design and Production Process Design 80s 90s

32 What is Digital Manufacturing? Digital Manufacturing represents an integrated suite of PLM tools that supports manufacturing process design, tool design, plant layout, and visualization through powerful virtual simulation tools that allow the manufacturing engineer to validate and optimize the manufacturing processes.

33 Where Does Digital Manufacturing Fit? Product Lifecycle: Design/Build/Automate/Maintain Product Domain Production Domain Org Processes R&D Materials & Product Research Design Engr Product Design Mfg Engr Process Planning Industrial Engr Work Flow, Mfg Processes Controls/Tool Engr Design, Produce Tools, Jigs, Fixtures, & Automated Systems Factory Operations/ Production Systems Obtain, Operate, Control, & Maintain Equipment & Automated Systems to Manufacture Products Operations Mgmt Function (Systems) CAD/CAE (Digital Def.) Specs, E-BOM, M-BOM Digital Mfg/Production Process Design, Virtual Factory Simulation CAM/NC Collaborative PDM Automated Assembly PLM Solutions: Interoperability & Collaboration Scheduling Resource Mgmt Mfg Intelligence Q/A Visibility Supply Chain Operations Engineering

34 Global Manufacturing Operations Enterprise Infrastructure Operations Infrastructure Design/Engineering Infrastructure = Manufacturing Node = Design Node

35 The end of virtualization and beginning of augmentation Product Lifecycle Processes Design Build Automate Maintain Mining Processes Digital Mining Solutions + MES Create Processes Plan Processes Simulate & Validate Processes Execute Processes Engineering Design Validate As Built Records

36 The integration with Shop Floor Execution ERP As Designed Process Data E-BOM M-BOM Process Creation Bill of Process Product Config. Product Design As Designed Product Data Tailored Work Package Process Configuration Work Instructions Work Flow Routing Operations Scheduling Shop Floor Requirements Data Exchange Performance Analysis Quality Assurance Labor/Parts/Tooling PLM Maintenance & Support Mine Mgmt Field and shopfloor Execution (MES) As Built Records

37 Digital + Shop Floor Execution = Augmented Mining Product Design CAD Collaborative PDM Closing the Loop From As-Built Records To As-Designed Product Data Management 3D Models E-BOM Process Planning Digital Mining Process Simulation & Validation Process Modeling Process Models,3D Simulations, Work Order Instructions Process Execution Shop Floor Execution (MES) Quality Assurance Unit Data & Work History Data Vault E-BOM Master Routings, M-BOM Work Order Release Work Orders Release & Status Material / Parts Inventory Mgmt Job Sign On/Off ERP Work Order Status Invoicing Shipping Production Scheduling Labor Reporting Financials Parts Purchasing Receiving

38 DESIGN Specification Requirements FABRICATE CONSTRUCT Procure Install OPERATE MAINTAIN Asset Life Cycle Modify Rebuild Monitor REPLACE RETIRE

39 Solution are not pure data driven?

40 Hybrid approach: physical + analytical Analytics Physics + Data Continuous & accessible + + Statistics Unlock trends, segment Physics Apply engine expertise = Industrial outcomes One platform for OT & IT teams to collaborate and innovate 40

41 Virtual assets with IIoT Future 1/26/

42 Asset management vs Virtual Now, machines asset become software-defined services Technical challenges to connect and make machines intelligent $ $ $

43 Application of virtual commisioning Supply Chain Network Data loop Virtual commissioning services Virtual assets Design feedback loop Service/Repair Shop

44 The virtual mine

45 What do we expect from the analytics?

46 The reaction is our expectation

47 Concluding remarks Validate Mining Processes, Production Systems, and operational resources through Virtual Commissioning prior to physical implementation Hybrid models are needed for virtual commisioning to deliver services O&M data is disparate and amount of this data is dramatically growing, same time assets are more and more complex (system of systems) RUL (Remaining Useful Life) was the driver for passive maintenance decisions, now prescription is proactive. Research is needed from component level to system level providing new O&M services by the means of asset big data analytics

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