Unplanned equipment failures in the oil and gas industry lead to significant financial losses, with millions spent on shutdowns and emergency repairs. Current maintenance systems lack the predictive accuracy needed to identify potential failures in critical machinery, such as gas compressors and turbines, before catastrophic incidents occur. Without early detection, operations are halted unexpectedly, causing disruptions in production and escalating costs. The industry requires a reliable, data-driven solution that can predict equipment failure well in advance, allowing engineers to plan maintenance proactively and avoid costly downtimes.
