Description
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.

Target Audience: PI Power User