Challenge :

Detecting temperature anomalies in an Expander unit to avoid physical breakdown.

Our team data analysts received a flagging notification which required an inspection into the temperature change in the bearing of an expander in an ethane separation plant in 2019. 

The data, obtained from our predictive learning software, reflected the abnormal influx in the lubricant oil temperature. This resulted in the significant drop of the overall health score, from 100% to 57%.

Solution :

Due to the early detection made by the data input and our computing software in critical time, our team of engineers reported to the plant engineers to further investigate the viscosity of the lubricant oil — which was the root cause of this issue. 

The discovery of this case’s irregularity managed to save the plant from experiencing possible damage to the bearing the Expander, unplanned downtime, and unnecessary maintenance cost.