Leveraging IoT and cognitive for asset and field force optimization_ibm
- 1. © 2017 IBM Corporation, IBM Leveraging IoT and Cognitive Capabilities in Field Operations and Asset Internet of Things (IoT) and Smart Grid--The 21st Century Technologies for the Power Sector 5 June 2017
- 2. © 2017 IBM Corporation, IBM l 2 The IBM Utilities POV outlines three strategic imperatives shaping the future of the industry 1. Viable Substitutes Rise introducing the business and technical challenges of intermittency, dispatchability and disintermediation WHAT WE SEE SHIFTING 2. Customer Engagement Deepens through rich and instant interaction, delivered via social and mobile apps WHAT WE SEE SHIFTINGWHAT WE SEE SHIFTING Alternatives reach grid parity in both capacity and price From centralized to distributed 3. Core Expectations Persist requiring the continued delivery of safe, reliable and low cost energy with sustainability embedded Energy intensity is sinking Prosumer supply is expanding Grid essentiality is challenged Agile new entrants emerging Growth stunted by #1 and #2 Embrace the role of energy integrator Engage customers as individuals Disruptive Innovation All three strategic imperatives are affected by the IOT megatrend, and a new approach that addresses the complexity from the proliferation of data
- 3. © 2017 IBM Corporation, IBM l 3 Case study: Transmission & Distribution Utility Asset Management & Analytics Overview • Electricity & gas generation, transmission and distribution • Core to the value chain are generators, import / connection points, transmission and distribution network and equipment and end customers (home & offices) Motivation Benefit & Value Regulatory framework’s goal is to ensure reliable and sustainable energy networks that give consumers value for money Enable supply of low carbon energy and reduction in Greenhouse gas levels aligning national mandate 20% of energy has to be from renewable sources. Less predictable & controllable nature of renewable energy requires flexible grid Many electricity assets are ageing and reaching to end of useful technical lives. Investment required for asset replacement / refurbishment Improved & more access to data – risk, criticality based approach, asset health, network risk modelling etc. Efficiently use non- invasive maintenance data (e.g. HD photos, infrared imaging, RFI monitoring) Optimize asset maintenance / investment and replacement choices A modular & integrated platform support future growth in analytics – faster deployment, data sharing, models etc.
- 4. © 2017 IBM Corporation, IBM l 4 Level of Excellence I. Run to Failure Maintenance V. Total Productive Maintenance IV. Reliability-based Maintenance III. Predictive Maintenance II. Preventive Maintenance Newcapabilitiesrequired Crisis management Fix it when it breaks; Unplanned / emergency maintenance Poor utilization of maintenance personnel High parts inventory Improved ability to review and report asset and network performance Ability to optimise an integrated delivery plan in real time Better and easier asset and operations data capture and sharing Improved planning and construction portfolio and Alliance management Better asset operation, maintenance and replacement decisions END-TO-END PROCESS VISIBILITY CONSISTENT, QUALITY DATA AND INTEGRATED PLANS Time-based maintenance Planned maintenance activities Inspections to eliminate equipment failure Increased inspections Reduced frequency and severity of unplanned failures Condition-based maintenance Increased equipment monitoring Reduction in unnecessary inspection and maintenance Further reduction of machine failures IT support improves personnel efficiency, assists buy / repair decisions and monitors repair quality Combines Preventive & Predictive Maintenance with Total Quality & Total Employee involvement Optimizes effectiveness, eliminates breakdowns, and promotes autonomous operator maintenance Organized statistical approach to maintenance Level of reliability planned based on equipment/process criticality Maintenance/ investment based on analysis of failures, forecast equipment use, history and general operating experience Regulatory framework has driven a re-assessment of how to manage assets… …with a consequent need for new capabilities
- 5. © 2017 IBM Corporation, IBM l 5 Phase 1 of the programme focused on capability in the following areas: Improved ability to review and report asset and network performance Ability to optimise an integrated delivery plan in real time Better and easier asset and operations data capture and sharing Improved planning and construction portfolio and Alliance management Better asset operation, maintenance and replacement decisions END-TO-END PROCESS VISIBILITY CONSISTENT, QUALITY DATA AND INTEGRATED PLANS 1. New analytic capabilities allowing improved and more access to data and information. For example, to support a risk and criticality based approach, asset health analysis, network risk modelling, etc. 2. A means to efficiently use the increased volume of non- invasive maintenance data and information e.g. RFI monitoring, infrared imaging, HD photography 3. Tools to make use of the geo-spatial data, integrated with other structured data that is currently available, e.g. route planning.. 4. Consistent tools to optimise asset maintenance/ investment and replacement choices, in order to deliver the opportunity for outperformance against incentives arrangements 5. A modular and integrated platform that will support the future growth in analytic activity: facilitating faster deployment, sharing of data, models and analyses and avoiding bespoke development of IS systems 6. Provide more consistent information, in a single place that all users can access more frequently and with less hand-offs (measure once use many times) ..delivered via a single focal point for all asset and network analytics and all workers, inc. mobile ones
- 6. © 2017 IBM Corporation, IBM l 6 In practice this means that we have to provide… Use structured and unstructured data Captured Detected Inferred Made consumable and accessible to everyone, optimized for their specific purpose, at the point of impact, to deliver better decisions and actions through: Descriptive Analytics Prescriptive Analytics Predictive Analytics What happened? Reporting What exactly is the problem? Visualisation How many, how often, where? Questioning What actions are needed? Decision support How can achieve the best outcome and address variability? Stochastic Optimization How can we achieve the best outcome? Optimization What if these trends continue? Forecasting What could happen? Simulation What will happen next if? Predictive Modelling Analytics Sophistication • Numeric • Text • Image • Audio • Video performance management strategic planning
- 7. © 2017 IBM Corporation, IBM l 7 Analytics and Optimisation frame work : Leveraging IBM Energy Analytics platform - Insights Foundation for Energy Intelligent Operations Centre Governance Policy Directives Workflows Standards Based Interfaces Domain Specific Interfaces InfoSphere EAM Maximo, SAP Other Gateway ECM Document Management Gateway Mobile Gateway/ Streams Sensor Data E.G. OSISOFT- PI KPI’sAlarms Data Models - CIM Service & Data Bus Geo-Spatial Alerts Reports/ Analysis Advanced Visual Features Visualisation Data IntegrationGateway Other: SAP –ERP Proj Mgt etc External Feeds: Weather Environmental … Event Rules Predictive Systems (SPSS) Modeling & Simulation Library (including 3rd Parties) Big Data Netezza Big Insights (hadoop) Models Optimisation Systems (ODME) 7 Dashboards DB2 & Informix Time series Required Optional Network View Gateway Energy Analytics Platform components
- 8. © 2017 IBM Corporation, IBM l 8 Enhanced Energy Analytics platform with IoT / Cognitive capabilities IoT for Energy and Utilities(IFE) - Platform as a service Asset Performance Management Connectivity Models Wind 360 Image Acquisition and Analytics Outage Detection Edge Analytics Worker Safety IoT for Energy and Utilities PID Industry Content, Analytics, Weather User Experience, Other Integrations Out of the box applications IoT Platform on Bluemix Custom application selected examples Data Sources Native Integration Connected devices
- 9. © 2017 IBM Corporation, IBM l 9 Analytics capabilities in Phase 1…. Roles & Permissions Data Imp/Exp Automated Emails/SMS System Monitoring (Alarms) Click to ActionEvent Mgt. (Alerts) Visualisation options Advanced Analytics + DST Data drill down Asset RAG status and scenarios Policy&planningcOperationsAllusersAllusers Mobile condition monitoring apps for iOs
- 10. © 2017 IBM Corporation, IBM l 10 Asset health tracking Health degradation prediction Risk and failure consequence Connectivity information Asset Performance Management ….
- 11. © 2017 IBM Corporation, IBM l 11 Moving towards an App store approach to analytics …. Platform allows usage of pre-built models and experimenting Risk Analytics Asset Health & Reliability Analysis Real Time Asset Monitoring Sensor AnalyticsSensor Analytics Risk PredictionRisk Prediction Spatio -Temporal & Emergency Scheduling Spatio -Temporal & Emergency Scheduling Spatial Risk AnalyticsSpatial Risk Analytics Capex & Opex Optimization Reliability ModelingReliability Modeling Asset Treatment Identification & Sustainability Analysis Asset Treatment Identification & Sustainability Analysis Failure Cause & Pattern Analytics Failure Cause & Pattern Analytics Asset Health AssessmentAsset Health Assessment Failure estimationFailure estimation Next Best Action IdentificationNext Best Action Identification Predictive Maintenance Planning Predictive Maintenance Planning Capital PlanningCapital Planning Distribution Grid MonitoringAutomated & Real Time Outage Planning Grid Improvement Optimisation Risk Analytics for Critical energy Infrastructure (RACE) Common Asset Decisions Replace or upgrade equipment Revise network topology Apply new technologies Revise maintenance strategies Improve equipment monitoring Increase Automation, IT, Enterprise Integration Improve Design standards Improve O&M practices At the Asset Level – Overload – Increase Rating – Upgrade – Monitor Condition – Extend Life – Replace At the System Level – System Topology – Operations – Maintenance Programs – Protection/Automation – Congestion Mgmt – Design Standards
- 12. © 2017 IBM Corporation, IBM l 12 Use Cases .. UC5: Thermal Rating UC6: S/S Real time monitoring ….. Maintenance Advisor, Worker health, Weather inputs… PoC Development Socialization Use Case Identification Platform Development UC1: RAG Dashboard UC2: DGAUC3: S/S SurveyUC4: Optimizer UC7: Deferred Maint. Risk Assessment Asset Health status Real time Condition Monitoring (Hourly Device Data) Mobile app For sub-station Survey, images capture, Anomaly detection Single view Planned, Unplanned, Forecasted work UG cable thermal Sensing with IoT device data Integration Optimize Capex S/S IoT Device data Integration Image Storage / Processing
- 13. © 2017 IBM Corporation, IBM l 13 Prescriptive & Cognitive analytics Awareness factors from weather, proximity • Analytics driven field worker • Role based – task orientated • Real time weather alerts • Training.. • Enhanced Safety through IoT/ Wearables Digital at the cornerstone of safety IBM/Apple Apps: Field Connect Asset Inspect Mobility technologies to deliver new levels of field work effectiveness and safety….
- 14. © 2017 IBM Corporation, IBM l 14 IoT / Cognitive capabilities Experimenting new use cases.. Visual Recognition Service for Asset Condition Inquiry 14 A way to complement/supplement IBM VR (“Watson") using domain specific models of regions of interest The system is able to recognize most of the beams (shown in red color) and rust (shown in green). The blue indicates the false alarms our system was able to discard, This model is not yet hardened and needs to be further improved using more domain knowledge and sophistication
- 15. © 2017 IBM Corporation, IBM l 15 IoT / Cognitive capabilities Weather inputs add value… 15
- 16. © 2017 IBM Corporation, IBM l 16 IoT / Cognitive capabilities Watson Field Operations Advisor.. 16 Value Propositions IOT and cognitive system mitigate skill and expertise shortage, also a tool for training new personnel Minimized time for manned-operations using as drones & robots. Increased time for unmanned operations Reduction of human error with improved accuracy, efficiency and reliability by bringing pertinent equipment and system information at the right time and right place Enabled event driven monitoring. Improved efficiency may facilitate more frequent operations IOT Opportunities Transmission Tower Visual Inspection – identify levels of rust/ energy leakage/ minimize Health & Safety risks Wind Turbine Blade Inspections – micro-cracking/ high resolution/ H&S minimized Vegetation Clearance (Powerlines/Pipelines) – using cognitive to pull together: visual images, regulatory requirements, plant species (growth rates) Solar Farm Visual Inspections and remote fault detection Ingested data including manufacturer manuals, utility procedures and Maximo work order repository Ecosystem Utilities looking to adopt latest technology Drone and cameras manufactures. Drones are used to inspect equipment status in hard to reach areas. Partnership with Spanish Drone maker and Aerialtronics are in the work
- 17. © 2017 IBM Corporation, IBM / MEA Confidential Santhosh S Nair ASEAN Leader, Energy & Utilities Mobile Email +60147298607 santhosh@my.ibm.com Thank You DISRUPTIVE TIPPING POINTS FOR UTILITY INDUSTRY IS QUITE VISIBLE….. EMBRACE DISRUPTION THROUGH DIGITAL OPERATIONS
- 18. Strategic Asset Management 18 Use Case Technical Delivery Benefit Est. Total Use Case 1 - Asset RAG Dashboard (Decision Support Tool for Totex planning) RAG Dashboard with asset health and maintenance details Cognos Reports Maintenance policy decision support tools to enable risk- and condition-based maintenance of substation assets $10.8m Use Case 2 & 3 - Dissolve Gas Analysis & Sub- station Survey Dashboard SoP based automated work flow Offline condition analysis and reporting of thermal and oil condition monitoring surveys to enable less intrusive maintenance practices $7.6m Use Case 4 - Single View of Plan Resource modelling and scenario planning capabilities Improve the optimisation of resource and outage requirements to deliver work $12.5m Use Case 5 - Thermal Ratings Management Analytic models to determine thermal ratings Bring the intellectual property associated with thermal ratings models in-house Increase the robustness and supportability of existing models $4.5m Use Case 6 - Risk Management (DMRA) Support for deferred maintenance risk assessments Overlays of condition, safety, compliance and alarms on Ops Diagrams Provide improved visibility of safety risk compliance to increase confidence in NG’s operations $2.7m Use Case 7 - Substation Monitoring Functionality to support online substation monitoring A more detailed view of asset condition will increase confidence in deciding when to maintain/replace assets Enabler of de-commissioning of the TSAM C3 platform $7.4m Strategic Asset Management Use Cases vs. ROI
- 19. © 2017 IBM Corporation, IBM l 19 Use Cases .. UC5: Thermal Rating UC6: S/S Real time monitoring ….. Maintenance Advisor, Worker health, Weather inputs… PoC Development Socialization Use Case Identification Platform Development UC1: RAG Dashboard UC2: DGAUC3: S/S SurveyUC4: Optimizer UC7: Deferred Maint. Risk Assessment Asset Health status Real time Condition Monitoring (Hourly Device Data) Mobile app For sub-station Survey, images capture, Anomaly detection Single view Planned, Unplanned, Forecasted work UG cable thermal Sensing with IoT device data Integration Optimize Capex S/S IoT Device data Integration Image Storage / Processing
- 20. 20 UC 1: Asset Health (RAG Dashboard) & Scenario Play • Both Core EAM & Condition Monitoring Data are taken care to asses individual asset health in Red, Amber & Green • Real time integration established with all sources of data • SPSS analytics implementation brought up the asset health status, reason and corrected action against each asset • Business user gets facility for scenario play to assess the need to change the asset health status Decision Support Tool for Annual Capital Planning
- 21. 21 UC 2: Dissolve Gas Analysis (Transformer OIL cond.) • Hourly device data from transformers • A dashboard with real time and history data • An analytics backed support tool aiding raising the defect automatically on any anomaly detection • Massive reduction in human effort on data collection & data analysis • Significant decrease in time to decision viewing thru the dashboard and related insight from history data trend Real time condition monitoring (device data integration +analytics)
- 22. 22 UC 3: Substation Asset Survey • Mobile app is provided to the field engineer to take the survey even at offline • Post survey, the data (reading) and images are pushed to analytics engine thru the high speed Aspera platform • SPSS engine identifies anomaly from the survey data and raises defects to the concerned engineer for action • A SoP based workflow takes care the defect resolution thru various levels • Engineer can see the defect details in a dashboard and can see the history trend for specific data point Substation Survey (Mobile App + Anomaly Detection + SoP based Work flow)
- 23. 23 UC 4: Single View of Plan • A single-window view for operational planning staff to view all planned, unplanned and forecast work and outcomes across maintenance and capital delivery. • This will provide a foundation for portfolio optimization Single view of plan for all the activities planned (Data Integration & Classification)
- 24. 24 UC 5: Thermal Rating • Real time thermal heat data from cable sensors • To provide accurate, reliable and complete thermal rating information to its SO counterparts. • Reduce the network constraint costs further by providing more detailed thermal rating data • Deferred capital needed for replacing assets by being able to increase capacity of existing network • Linkages to limit overload exposure to client’s owned damaged assets Real time over heating monitoring for underground Cables (IoT device data integration + anomaly detection + downstream processing)
- 25. 25 UC 6: Substation Real Time Monitoring • Sensor reading collection and processing from all the devices • Data analysis, visualisation and monitoring of Substations’ assets within single system • Communications networks built for sending alerts notifications via email and SMS to Field Support Engineer. • Hot Joint model data storage within SAM and image within client’s ECM system.