GTAA IoT Pilot Project Real Time Asset Health Analytics

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1 GTAA IoT Pilot Project Real Time Asset Health Analytics ACI-NA Conference Fall 207 Mr. Tulio A. Bernales, P.Eng, FMP Manager, Asset Management Airport Planning & Technical Services Greater Toronto Airports Authority Mr. Joseph Mahaz President & CEO Electronic Data Inc. (EDI) Mr. Jeth Harbinson Engineering Director Business Development Arrow

2 Back Ground Early 206 EDI, IBM & Arrow partnered to formulate an IoT strategic plan. Team set out to answer 3 questions: ) What is IoT 2) Use case in Airports 3) How to bundle as an IoT, Predictive Maintenance package Goals: ) Reduce Failures 2) Increase Wrench Time 3) Extend Asset Life

3 AGENDA Introduction About Toronto Pearson Problem Statement Solution (IoT) / Expected Results Why? IoT Pilot Project Scope Project Architecture Implementation Next Steps Current Status Gathering of Data PMQ Predictive Maintenance Feasibility Lessons Learned

4 About Toronto Pearson Largest Airport in Canada with 80+ destination 44+ Million Passengers 7.% Avg. Growth in the Past 3 Years 440,000+ Aircraft Movements 45% of Canada s Air Cargo 23 rd Busiest Airport by Total Passenger Traffic 5 th Busiest Airport by Flights 00% Self-funded (no government funding) 2 nd Largest International Airport in North America (second only to JFK)

5 Toronto Pearson Assets $7B GTAA Managed Assets 00+ Passenger Boarding Bridges 22+ KM of Baggage Handling Systems 7MW Co-Generation Plant 2 Terminals & 5 Runways 300+ People Moving Devices Cable Driven Automated Terminal Link Train 200M SQFT of Airport Land Sewage Lifts & Storm water Facilities

6 Toronto Pearson Challenge of Growth Passenger Growth Impact on Airport Impact on Asset Health Maintenance Impact Monday 03:00 06:00 09:00 2:00 5:00 8:00 2:00 Monday 23:59 Washroom Maint. 3 HRS 58.M Baggage System Maint. Boarding Bridge Maint. 6 HRS HRS 43M 52.7M Passengers by 209 Passengers by 202 Electrical System Maint. Structural Door Maint. 4 HRS 4 HRS Re-Lamping Maint. 3 HRS Passengers In 206 8,000 PAX / Day in ,000 PAX / Day in 202 Shorter Maintenance Windows Pit Stop Precision & Efficient Maintenance

7 Problem Most Organizations have Tons of DATA but not the RIGHT data or USEFUL Information In 206 GTAA Dispatch Centre Handled: PWC: 386 Dispatch Calls ESC/MW: 8338 Dispatch Calls T/T3 BHS: 9286 Dispatch Calls 2 Approximately % of components of a complex asset fail over time. 89% fail randomly over time.

8 Solution (IoT) The Internet of Things (IoT) is the networking of physical devices or "things" embedded, with electronics, software, sensors, and connectivity to enable objects to collect and exchange data. Experts estimate that the IoT will consist of almost 50 billion objects by 2020 IoT helps organizations move to advanced stages of Asset Management Addressing the 89% random failures with a combination of Asset Data, Operations Data, Maintenance Data and supported by Technology (IoT and PMQ) is key to being a leader in this challenge

9 EXPECTED RESULTS Using IoT (Impact) On-Demand Maintenance Typical Maintenance Programs Reactive Calendar Based Maintenance Scheduled Usage Based Maintenance Preventative Condition Based Maintenance Real Time Asset Health Analytics Short Term Cognitive Predictive Information Long Term (Maintenance Maturity / Time)

10 What is Cognitive Analytics There are three capabilities that differentiate cognitive systems from traditional programmed computing systems Understanding Cognitive systems understand like humans do, whether that s through natural language or the written word; vocal or visual. Reasoning They can understand information but also the underlying ideas and concepts. This reasoning ability can become more advanced over time. Learning They never stop learning. As a technology, this means the system actually gets more valuable with time. They develop expertise.

11 Intelligent Assets Understanding I am not feeling well today. My sensors are telling me I am overheating and have unusual vibration. My last service was four days ago and the service completed was a motor replacement. My next scheduled maintenance is in three weeks Reasoning Based on past failure conditions, I know I am going to fail within the next (3) Days! Learning I suggest you replace my drive bearing

12 Extend Asset Life Improve Reliability Improve return on investment Minimize downtime/failure Increase service availability WHY? Value Proposition Optimize Maintenance Customer Experience Optimize asset performance Reduce operating cost Reduce Customer Impact Best Airport in the World

13 Project Scope GTAA has teamed with EDI, Arrow Electronics and IBM to implement an IoT pilot project which will deliver real time data for asset health monitoring and predictive failure analytics. The pilot will be focused on three key customer facing systems: 2 Passenger Boarding Bridge Potable Water Cabinet People Moving Devices Moving Walk Escalator 3 Baggage Handling System Power Turn Conveyor

14 Project Architecture Publish and Subscribe based flow Sensors connect to Arrow Connect Gateway Arrow Connect Gateway publishes sensor messages to IBM Cloud Sensor data is rendered into dashboards using IoT Platform and Node Red Node Red used to apply logic and rules to the data so that automatic work orders are produced in Maximo Asset meter objects collect data from the IoT Platform by subscribing to meter events Data is stored in a cloud based database for longer term historic analysis ( Required for predictive analytics) Arrow Electronics Sensors Arrow Electronics Gateway IBM BlueMix Cloud GTAA Firewall protected internal network Public Internet Public Internet Windows Server IBM Maximo IBM Node Red Proxy

15 Project Architecture IBM BlueMix/Node Red Converting Data to Information using Rule Based Analytics

16 Implementation (Moving Walk Sensors & Communication) Device Description Qty GW Gateway TS Ambient Temperature TS2 TS3 Gearbox Temperature Motor Temperature TS TS2 TS3 MH Motor Vibration/Acoustic MH WS Fluid Leakage ENC IP-66 Enclosure WS PS DIN Rail Power Supply

17 Implementation (Escalator Sensors & Communication) Device Description Qty GW Gateway TS Ambient Temperature TS2 TS3 Gearbox Temperature Motor Temperature TS TS2 TS3 MH Motor Vibration/Acoustic MH WS Fluid Leakage ENC IP-66 Enclosure WS PS DIN Rail Power Supply

18 Implementation (PWC Sensors & Communication) Device Description Qty TS, Heating Pad 2 TS2 Temperature TS3 Pipe Temperature TS4 Backflow Cabinet Ambient TS5 Cabinet Ambient WS Drain Leak Detection FS Water Flow Detection DS..DS 2 Door Open Sensors x2 (Left, Right) GW SBC/Sensor Gateway ENC IP-66 Enclosure PS DIN Rail Power Supply DS TS WS FS TS4 DS2 TS2 TS3

19 Implementation (BHS Sensors & Communication) Device Description Qty GW Gateway TS Motor Temperature MH MH Motor Vibration Acoustic TS ENC IP-66 Enclosure PS DIN Rail Power Supply

20 Current Status Sensors have been installed and tested on the BHS, PWC, two PMD s Sensor data is being published to the IBM IoT Platform (Bluemix) Dashboards present real-time and historic views of the data Rules defined to automate the generation of work orders in Maximo and alerts via

21 Next Steps Gathering Data IoT SENSORS IoT is not just about collecting lots of data and doing corrective maintenance it s more about getting the right data that will yield long term historical analytics. PMQ BI ANALYTICS Asset If you have the right data (and we do!) we can enrich that data with all other data sources to move towards predictive analytics. Predictive Maintenance VISUALIZATION/ DASHBOARDING BLUEMIX PLATFORM Our goal is to significantly reduce breakdowns and unplanned maintenance and move to a more condition based and predictive based maintenance paradigm. Feasibility Study Sensor/WO/Fault/Failure Data DATA WAREHOUSE Sensor Data Fault Data SCADA Finally. need to determine what areas will return highest return on investment. Study needs to compare current failure rate and the impact/risk to the operation to support investment. Questions to be answered; will this become an overlay to current system or a requirement for systems moving forward? PREDICTIVE ANALYTICS Prescriptive Maintenance Work Order Data CMMS

22 Predictive Maintenance Analytics upload analyze report & recommend act aggregate Identify and manage asset reliability risks that could adversely affect plant or business operations

23 Predictive Maintenance Analytics Assess performance of assets in a specific class Identify assets needing attention Use analysis to guide maintenance strategy and planning

24 Predictive Maintenance Analytics Machine learning identifies drivers and risk factors that influence asset health Use drivers and risk factors to develop or refine asset maintenance strategy

25 Predictive Maintenance Analytics Chronologically compare maintenance, failures and drivers to start identifying patterns.

26 Visualizing Data Through BI Tools

27 Lessons Learned An IoT project spans the line of business and the IT departments it is essential that you collaborate with all parts of the business in our case, this included EDI, Arrow, GTAA IT and GTAA Maintenance (and IBM). If you don t collaborate you will hit multiple brick walls. Choosing the right sensor is very important and took a lot longer than we initially thought it would Consider the total IT infrastructure organisations like an Airport run very secure networks with multiple firewalls and restrict anything that is not business critical. Watson IoT requires certain ports to be opened that are not standard make sure you take this into account Never assume that, just because there is an Ethernet cable close by, it is connected to a gateway

28 Thank You Questions & Discussions

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