PRESENTATION
2020 - OSIsoft PI World San Francisco - Manufacturing and Transportation
Manufacturing & Transportation Q&A Session - Increase OEE by Combining PI System Sensor & Maintenance Data
This presentation outlines how OSIsoft's PI System data is leveraged to provide key information about an asset’s health and can be combined with maintenance records to improve operational efficiency. Every time an asset fails, the operational efficiency is being affected, as it depends on the asset readiness and availability. To improve efficiency, an effective maintenance plan is needed and that requires a significant amount of clean and reliable data. Getting access to such data can be very challenging. Prenscia Engineering Solutions is working with the OSIsoft team to provide a complete solution that can be implemented without spending years of planning and execution. Our approach combines both operational data (PI System) and maintenance data (CMMS Systems) to improve the data quality, capture and verify the maintenance events, perform criticallity analysis and assess the health of the asset.
Industry
- Facilities & Data Centers
- Manufacturing
Company
HBM Prenscia
Speaker
Vasileios Geroulas
Vasileios Geroulas is an Engineering Solutions Consultant for HBM Prenscia. He is focused on developing Asset Performance Management solutions, utilizing his expertise in Engineering (Reliability, Vibrations, Fatigue) and Data Science. Vasileios has Ph.D. and MSc degrees in Mechanical engineering and BSc and MSc degrees in Electrical Engineering.
Company
HBM nCode Federal
Speaker
Guga Gugaratshan
Guga Gugarashan has extensive industrial experience in gasoline and diesel engines and vehicle operation. He also managed manufacturing plant and test & research engineering operations. His expertise includes reliability, engine development, data acquisition, lean manufacturing, structural dynamics, and data analytics. Guga has a bachelor’s and a Master’s degree in Mechanical Engineering. Currently, he is pursuing another Degree in Applied Data Science at the University of Michigan – Ann Arbor.