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IBM Maximo Predictive Maintenance FMMUG 2018

IBM Maximo Predictive Maintenance FMMUG 2018

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Introduction to IBM Watson IoT for Predictive Maintenance and Optimization with Maximo
Speaker: Andrew Condos, IBM

Overview: Optimizing asset performance is crucial to the cost effective and efficient operation of asset-intensive organizations. Scheduled maintenance procedures help to achieve this goal, but the key to unlocking real productivity improvements is in predicting how an asset operates over its entire life-cycle.

This session will introduce you to how companies are using IBM Watson IoT Predictive Maintenance and Optimization with Maximo to identify and manage asset reliability risks that could adversely affect plant or business operations. IBM PMO with Maximo applies machine learning to prescribe actions based on predictive scoring, identifies factors that positively and negatively influence asset health and delivers a detailed comparison of historical factors affecting asset performance.

Introduction to IBM Watson IoT for Predictive Maintenance and Optimization with Maximo
Speaker: Andrew Condos, IBM

Overview: Optimizing asset performance is crucial to the cost effective and efficient operation of asset-intensive organizations. Scheduled maintenance procedures help to achieve this goal, but the key to unlocking real productivity improvements is in predicting how an asset operates over its entire life-cycle.

This session will introduce you to how companies are using IBM Watson IoT Predictive Maintenance and Optimization with Maximo to identify and manage asset reliability risks that could adversely affect plant or business operations. IBM PMO with Maximo applies machine learning to prescribe actions based on predictive scoring, identifies factors that positively and negatively influence asset health and delivers a detailed comparison of historical factors affecting asset performance.

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IBM Maximo Predictive Maintenance FMMUG 2018

  1. 1. Predictive Maintenance Enhance your Maximo investment with predictive capabilities to further improve productivity, determine asset health and reduce risk Andrew Condos Senior Product Consultant – Watson IoT Analytics FMMUG18 :: Seattle 1
  2. 2. IBM Predictive Maintenance and Optimization (PMO) complements IBM Maximo to obtain greater value from critical assets Maximo • Support maintenance activities from initial service requests through planning, completion, recording of results • Capture details about asset infrastructure, spare parts, performance, and work history • Plan demand, streamline purchasing processes, ensure contract compliance • Prescribe lowest-cost, highest-utilization workforce schedules • Reduce overall risk, comply with appropriate regulations Predictive Maintenance • Models calculate asset health scores and predict asset life spans • Real-time interactive dashboards monitor assets and processes • Detect asset failures and quality issues earlier than standard statistical control processes • Explore asset performance information to determine root-cause of failure • Provide optimized maintenance recommendations to operations and maintenance personal FMMUG18 :: Seattle 2 work & asset management, planning & scheduling, supply chain, health & safety asset + instrumentation + data + connectivity + analytics + monitoring + reporting
  3. 3. MAHI vs Predictive FMMUG18 :: Seattle 3 MAHI Predictive Knowledge base Known causations / Tribal knowledge Historical data Time to Value Immediate Dependent on data sources Logic Formulas Predictive algorithms – structured & unstructured data Data requirements Minimal to start 6 months or greater Maximo dependent Yes No Application Condition Assessment Preventive Maintenance Repair and Replace Predictive & Prescriptive Maintenance Advanced Algorithms Reliability Engineering
  4. 4. © 2015 IBM Corporation Maximo Asset Management Predictive Maintenance description, location, material types, processes, products, suppliers… 4 Base data from Maximo used in Predictive Maintenance
  5. 5. © 2015 IBM Corporation Analyze data to develop predictive models data sources identify relevant data sources develop optimum model(s) apply modeling algorithms maintenance sensor health top failure reasons integrated health feature based custom ensemble industry-specific assets 5 • maintenance logs • inspection reports • repair invoices • customer complaints • warranty claims • operator profiles • test results • maintenance logs • inspection reports • repair invoices • customer complaints • warranty claims • operator profiles • test results
  6. 6. © 2015 IBM Corporation Transform asset performance data into action 6 initiate work order with recommended actions modify maintenance schedule modify production schedule conduct root cause analysis review operator procedures modify process design initiate service call develop new service or warranty program optimize parts inventory & locations initiate supplier review conduct root cause analysis modify process design modify product design • maintenance logs • inspection reports • repair invoices • warranty claims acquire analyze model optimize decisions asset performance product quality monitor act model
  7. 7. © 2015 IBM Corporation Acquire data to gain understanding of asset performance 7 unstructuredstructured • maintenance logs • inspection reports • repair invoices • customer complaints • warranty claims • operator profiles • test results device-,asset-andindustry- specificanalytics&models • Which data best predict performance? • What are the main factors for failure? • When is this asset most likely to fail? • What is the optimum maintenance schedule? • What are the most effective repair procedures? • What are the key variables in manufacturing variance? • Should we modify the warranty program?
  8. 8. © 2015 IBM Corporation Automatically initiate or update Maximo work orders with recommended actions Nidal Cruz A. Withers When removing the PM rotating assembly from the motor care must be taken to overcome the inherent magnetic forces that will try to hold the rotating assembly (rotor and shaft) in the stator winding. It is recommended that the motor be disassembled and reassembled in a vertical drive end shaft up position using a hoist to remove the rotating assembly. In the horizontal position first remove any accessory items (fans, blower, feedback devices, etc.) Also remove the bearing inner cap bolts (if provided). Mount the motor in a vertical drive end shaft up position and remove the drive end bracket. The opposite drive end bracket can remain installed. The thread in the end of the shaft can be used with an eye bolt to lift the rotating assembly with the hoist out of the frame/winding stator. North Shore Inspection BluMark Beta-Q update an existing work order with maintenance recommendations 8
  9. 9. © 2015 IBM Corporation Predictive analytics can benefit any asset- intensive industry 9
  10. 10. Demonstration FMMUG18 :: Seattle 10
  11. 11. Q&A FMMUG18 :: Seattle 11

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