Remote Monitoring, Digital Twins, and AI on the Plant Floor

Sponsored by FS-Elliott

Digital twins and AI-driven analytics are already being deployed to monitor compressor performance, but how well is that technology actually working on the ground? Wael Al Mandarawi points to real-time data as a key tool for catching problems before they become failures, noting that the software his team selected can flag efficiency degradation as much as six months in advance.

Omar Al-Essa goes further, describing how digital twins and AI turn what used to be sudden, catastrophic failures (gearbox tooth breaks, bearing seizures, seal failures) into slow-moving trends that can be spotted weeks or months out. He shares an example of a tool that compares real-time performance against original equipment curves, and explains why early detection matters even more for remote and offshore facilities already contending with spare-parts logistics and geopolitical supply chain pressure.

This episode is essential viewing for reliability teams evaluating whether digital twin and AI investments are living up to their promise in the field.