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Introduction
Artificial intelligence is no longer an experimental tool reserved for research labs—it is actively reshaping how maintenance is performed on the plant floor. For years, predictive maintenance relied on sensors, inspections, and human judgment. Experienced technicians could sense a problem by the vibration of a motor or the smell of overheated oil. Yet even the sharpest instincts have limits. What happens when a machine gives off no clear warning until failure is imminent?
Today, AI-powered systems bridge that gap. By processing thousands of data points per second, they identify patterns too subtle for human perception. A minor uptick in motor current, a microsecond delay in turbine response, or a shift in compressor seal dynamics—all can be captured, analyzed, and acted upon before they escalate into costly downtime.
But AI in maintenance isn’t about chasing shiny new tools. It’s about turning complexity into clarity and uncertainty into foresight. In this edition, we’ll explore how AI-powered maintenance is transforming practice. You’ll take a deep dive into AI-enabled workflows, spotlight LNG compressors as critical assets, see how IoT anomaly detection scales predictive maintenance, and finish with an expert analysis of AI-driven cost savings. By the end, you’ll know what steps you can take today to put AI to work for your reliability program.