Following the Data Journey for Ongoing Asset Reliability
Traditional asset reliability programs rely and focus on periodic assessments and interventions. Expert audits rank asset criticality and then develop preventive maintenance programs and perform failure root cause analysis, often after the fact of mechanical breakdown.
New technologies now enable a deeper, ongoing understanding of system performance, potential equipment failures and specific local factors that may contribute to equipment underperformance or failure. These technologies include the growing availability of sensor data, less expensive data acquisition and storage, advanced computing technology, and the wide availability of machine learning/data science tools and techniques used to rapidly analyze, understand and improve system reliability.
This webinar will outline the primary elements of the data journey of a reliability system and provide specific examples of how to implement and manage such a system, as well as the key benefits of such an approach.
What We Will Cover
The discussion will focus on a few key elements:
- Data Capture
- Applied Models and Methods for Performance Assessments
- Business Process Analysis with Data to Provide New Equipment Insights
Desired results to be discussed and explored include:
- A deeper understanding of asset performance vs. averages
- A continually improving detection system for equipment condition
- The ability to augment existing reliability and maintenance programs with new data-driven approaches, including machine learning insights based on empirical models
- How live analytics allow organizations to understand the essence of the physical behavior of the equipment in operation in its full complexity
Mark Wolfgram, GenesisSolutions
Mark Wolfgram is a PM and Senior Reliability Manager for GenesisSolutions, an ABS Group company, specializing in asset reliability and leveraging enterprise asset management (EAM) systems to make data-driven decisions. He has extensive knowledge of EAM processes and practices and has held positions in engineering, maintenance, operations, logistics and quality throughout a career spanning over 30 years. Wolfgram earned a BS in Mechanical Engineering from Purdue University and completed post-graduate studies in advanced vibrations and acoustics, statistical methods and business management. He is a member of the Society for Maintenance & Reliability Professionals (SMRP) and is a Certified Maintenance & Reliability Professional (CMRP).
Roy Keyes, Arundo Analytics
Roy Keyes is Lead Data Scientist at Arundo Analytics, a software company enabling large-scale machine learning applications in heavy industries. In this role, he leads Arundo's North America data science team. An expert in advanced system analysis and analytical methods at the intersection of cloud software, data science and industrial operations, Dr. Keyes hold a BS in Physics from Rice University, an MS in Nuclear Engineering and an MS and
For over a decade, Genesis Technology Solutions, Inc. (GenesisSolutions), the Asset Performance Optimization subsidiary of ABS Group, has been a market leader in asset reliability, asset maintenance