IMC-2019 Presentation 38:44 Minutes

by John Ganaway, Jacobs

Uptime Elements' Asset Condition Management knowledge domain includes several different methods for collecting and analyzing data. In addition to data collection and review, there are a few other key elements to any program, including program success metrics, technician training, data storage, and program interval based review or assessment.

Recognizing that a vibration data collection and analysis program is the cornerstone of many asset condition management programs; understanding the recipe for assessing your program is key. This assessment relying heavily on the content found within the book “Audit It. Improve It! Getting the Most from Your Vibration Monitoring Program" by Alan Friedman, available at the MRO-Zone Bookstore.

This presentation will focus on the process for assessing an asset condition management program using a recent vibration analysis program assessment as a template. Elements that are explored include goals, people, leadership, tools, understanding, data collection, data analysis, reporting, follow-up and review, process and procedures.

Learning Objectives:

  • Resources for vibration program assessment
  • Review process steps
  • Program successes and missed opportunities
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IMC Presentation (53:08 minutes)

by Richard Wurzbach, MRG Labs

American Axle and Manufacturing Upgrade and Consolidation Story with Zero Production Downtime

American Axle and Manufacturing Upgrade and Consolidation Story with Zero Production Downtime

Best practices for a Maximo upgrade – timeline and budget Challenges overcome and lessons learned Business case for zero downtime tool – how it impacted upgrade and operations
Establishing Expectations and Requirements for Performance Digital Twins

Establishing Expectations and Requirements for Performance Digital Twins

Jared Hartness, GE Digital

Implementing digital twins is becoming a strategic necessity for many industrial companies. In this session, learn the basics of how digital twin models should be built, how to scale and implement these models on assets and systems to create a twin, and discover best practices for how to leverage these twins operationally to predict, diagnose and forecast performance degradation as a part of your reliability work processes.

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