Predictive maintenance

Smarter decisions: the impact of condition monitoring and predictive maintenance

Predictive maintenance involves the use of sensor data, historical maintenance records, and other relevant information to predict process behavior and potential equipment failures. Leveraging big data, machine learning, data analytics, and modeling, predictive maintenance anticipates potential issues, schedules maintenance tasks more efficiently, and reduces the likelihood of unplanned downtime and costly repairs.

Predictive maintenance solutions effectively detect changes in equipment performance and condition by relying on real-time information. This approach provides access to the status of individual assets, processes, or even entire smart factories. A proactive maintenance strategy significantly enhances efficiency by minimizing unnecessary manual maintenance tasks, ultimately leading to fewer failures and reduced downtime.

Predictive maintenance is also a gateway to innovative business models. By offering "availability as a service," companies can transform their business model, for instance, from merely selling motors to ensuring continuous machine rotation. This strategic shift is not limited to mere transactions but can help create enduring partnerships to enhance visibility, optimize maintenance, and guarantee sustained machine availability.

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