What kind of data is typically used in predictive maintenance?

Predictive maintenance relies heavily on sensor data that monitors the health and performance of equipment. This type of data is critical because it provides real-time insights into machinery operations, allowing for the early identification of potential failures before they occur. By analyzing metrics such as vibration, temperature, pressure, and other operational parameters, maintenance teams can predict when equipment is likely to require servicing or could fail, thereby minimizing downtime and reducing maintenance costs.

In contrast, while financial data can inform investment decisions, it does not give direct insights into the physical conditions of machinery. Market research data focuses on consumer behavior and trends rather than equipment performance. Customer satisfaction surveys provide feedback on service quality but are not relevant to the actual mechanical health of equipment. Thus, sensor data is the fundamental aspect that underpins effective predictive maintenance strategies.