Unexpected equipment failures are costly and disruptive. Predictive maintenance uses sensors on machines, measuring vibration, temperature and more, and AI models that learn the normal patterns, so they can flag early signs of wear and warn that a part will likely need attention before it actually breaks.
Fixing a problem on a planned schedule is cheaper and safer than reacting to a sudden failure, and it avoids replacing parts that still have life. The models need good sensor data and a period of learning to be reliable, and a warning is a probability not a certainty, so skilled staff still decide what to act on, informed by the system's alerts.