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ASQ RRD Series Webinar: The Use Of AI In Predictive Maintenance

By Tim Gaens posted 05/31/24 05:24 PM


ASQ RRD Series Webinar: The Use Of AI In Predictive Maintenance

Presenter: Frank Juarez

Thursday, July 11, 2024 12:00 PM – 1:00 PM
(UTC-04:00) Eastern Time (US & Canada)


Artificial Intelligence (AI) technologies can significantly benefit predictive maintenance (PdM) strategies by offering transformative opportunities for industries striving to enhance operational efficiency and reduce downtime. AI-enabled predictive maintenance leverages machine learning algorithms, which are capable of ingesting historical and sensor maintenance data to forecast equipment failures before they occur. This allows timely interventions that can help identify reliability drivers, failure modes, and the most effective maintenance tasks that can in turn minimize unscheduled downtime and maintenance costs. Intelligent data processing and pattern recognition from AI can also help optimize maintenance schedules and strategies based on real-time operating conditions and historical performance data more efficiently than traditional methods. This results in more accurate and timely decision-making, supply and logistics synchronization, resource allocation, overall system reliability, at a lower cost, but only if the ingested data is of sufficiently quality. This webinar will provide an overview of common PdM strategies, AI integration methodologies, benefits, and challenges, underscoring its potential to revolutionize maintenance paradigms by fostering predictive insights.

Pesenter bio:
Frank (Matthew) Juarez is a seasoned engineer with 15 years of experience in reliability engineering. Since 2022, Frank has served as the Chief Reliability Engineer and Manager at Redhorse Corp. located in Arlington, Virgina. He earned his B.S. in Mechanical Engineering from the University of Texas at San Antonio, followed by an M.S. in Aeronautical Science from Embry Riddle Aeronautical University. Within Redhorse Corp.’s National Security Program, he provides reliability engineering oversight, technical guidance, strategic planning, team management, and facilitates the development of predictive maintenance capabilities and solutions in support of Department of Defense CBM+ Programs. Previously, he spent over a decade at an aircraft engine maintenance and depot facility in San Antonio, working on RCM programs for the T56, J85, and F100 engines. Other experience includes repair development engineering for commercial airline APUs, other small gas turbines, and the USAF T-38 aircraft. Frank is also an active member of numerous professional groups, such as SAE’s Integrated Vehicle Health Maintenance committee and CBM+ Working Group, IEEE, AIAA, and PHM Society