Background

Periodic maintenance and downtime has its importance to ensure a healthy status of asset whilst ensuring operations sustains for a long time. But, unplanned/sudden breakdown or deferments results in downtime leading to production loss, costly maintenance procedures (if not attended properly) impacting organization economics. The idea of reactive maintenance may not always be the safeguarding step for, any action to deviation without visualization puts personnel in state of blindness leading to safety hazards.

But, don’t worry!!!

Solution

We have a solution to help you tackle these scenarios smartly using predictive techniques and visualize anomalies better using PI vision. Predictive Maintenance is an​ application developed on the C3.ai (a Platform providing a suite of AI apps) that gives advanced failure prediction for asset components such as valves and compressors thus, reduces the need for expensive reactive responses. This technique collaborates effectively with PI vision to visualize anomalies being detected, develop and deploy models in C3.ai based upon historical data analysis from past 3-4 yrs. PI screens are best for day-to-day work as it helps to go through all parameters in ONE GO. The asset operator and remote asset support engineer can work effectively together, through monitoring of different parameters like dry gas seal, process, and equipment health parameters of the compressor, turbine, pumps, and gearbox. the data collected serves a great purpose to devise models in C3.ai and any gaps or downtime in PI can significantly impact data quality , if not addressed in time.