Technology used in **predictive maintenance** for machinery typically involves IoT sensors combined with data analytics. These IoT devices collect real-time operational data, such as vibration, temperature, and pressure, from machinery. The information is then analyzed using machine learning algorithms to predict potential failures or maintenance needs before they occur. This proactive approach minimizes downtime, enhances equipment lifespan, and reduces maintenance costs by ensuring timely interventions rather than relying on reactive maintenance strategies, ultimately leading to more efficient and reliable operations.