Predictive maintenance uses equipment condition and performance data to estimate when maintenance may be needed. The goal is to plan an intervention based on evidence of changing condition rather than relying only on a fixed calendar or waiting for a breakdown.
Teams may analyze vibration, temperature, electrical signals, oil samples, alarms, or operating history. The useful signals depend on the asset and the failure modes being monitored.
A prediction is an estimate, not a guarantee. Reliable decisions depend on good data, suitable thresholds or models, and enough warning time to plan the work. Predictive maintenance is one application of connected manufacturing technologies associated with Industry 4.0.