In this lab, we walk through scenarios to illustrate the use of process data and machine condition data for usage-based, condition-based and predictive maintenance. Data sources include traditional plant instrumentation such as PLCs and SCADA, the newer IoT devices, and from machine condition such as vibration, oil analysis etc.
Usage-based maintenance includes using operational metrics such as motor run-hours, compressor start/stops, grinder tonnage etc. And, condition-based maintenance utilizes measurements such as filter deltaP, bearing temperature, valve stroke travel, and others. Predictive maintenance can be using simple analytics such as monitoring vibration (rms, peak etc.) to predict RUL (remaining useful life) or heat-exchanger fouling to schedule cleaning etc. The lab will also reference and discuss predictive maintenance use cases that require advanced analytics such as APR (advanced pattern recognition), anomaly detection, and others.
Content Preview: https://pisquare.osisoft.com/community/all-things-pi/blog/2020/05/05/usage-based-condition-based-and-predictive-maintenance-with-the-pi-system-a-layered-analytics-approach
Target Audience: PI Power User
Gopal Gopalkrishnan, PE has been involved in several roles at OSIsoft and has been working with the PI System since the mid-1990s in software development, technical and sales support and field services. Attached to the Philadelphia office, he is presently a Solution Architect in the Partners Group. Previously, he was a Product Manager with a focus on Enterprise and Asset Integration and PI Data Access. Gopalkrishnan has a master's degree in chemical engineering, continuing education in business administration and is a registered Professional Engineer in Pennsylvania. He is also active in the MESA Technical Committee and the MESA Continuous Process Industry Special Interest Group and active in topics such as data mining, energy efficiency, manufacturing intelligence, sustainability, including green initiatives in facilities and data centers.