Introduction
A maintenance strategy determines how consistently a plant runs, how much downtime it experiences, and how efficiently teams can manage equipment over time. Many factories still confuse the roles of predictive vs preventive maintenance, even though both approaches are meant to avoid failures. The rise of industrial AI is pushing modern plants toward data-driven systems that can anticipate issues rather than relying solely on schedules or manual checks.
This infographic explains what is the difference between predictive and preventive maintenance and how each approach impacts production stability, costs, and long-term reliability.
What Is Predictive Maintenance
Predictive maintenance uses continuous data, sensors, and industrial AI models to understand how an asset is behaving in real time. Instead of relying on fixed intervals, it detects subtle changes in vibration, temperature, load, and other performance signals to warn teams before failure takes place.
This approach reduces unplanned stoppages, avoids unnecessary servicing, and allows teams to act only when the asset truly requires attention.
What Is Preventive Maintenance
Preventive maintenance is a schedule-based approach where equipment is serviced at fixed intervals. These intervals may be based on OEM recommendations, industry standards, or experience. The goal is simple: do maintenance before an expected failure window and avoid sudden breakdowns.
Conclusion
A straightforward comparison of predictive vs preventive maintenance shows why modern factories are shifting toward data-based systems. Preventive maintenance provides structure, but predictive maintenance delivers precision. For manufacturers looking to balance uptime, cost, and reliability, predictive maintenance powered by industrial AI offers the strongest path forward. With platforms like ProHM+, plants can move from guesswork to intelligent monitoring, ensuring every asset runs at its highest possible stability.