Any activity based on the use of equipment, to keep its technical assets fully efficient, must implement regular maintenance plants (scheduled or preventive maintenance) in order to avoid failures and/or breakdowns that might cause downtime (failure-driven maintenance). Such issues result into expensive repairs, up to the replacement of entire machines, with a significant impact on general costs.

Unlike preventive maintenance, based on planned actions, predictive maintenance is based on the assessment of operating parameters to plan maintenance based on real conditions (CBM – Condition Based Maintenance).

With the Alsiter project, Sadas Group and Sei Sistemi offer advanced technical solutions for the analysis of operating parameters, including fixed or mobile monitoring systems, in order to optimize maintenance operations and increase the efficiency of production lines.

In predictive maintenance operations, the sensors of a machine collect data about its performance. These sensors can monitor and collect real-time data about temperature, health, pressure, vibration and production rates of a specific machine. The sensors then send such data to a processing unit, detecting possible deviations from preset values or machine learning models.

In detail, Alsiter can execute the following tasks:

  • Thermographic surveys to identify critical situations and monitor plant efficiency
  • Analysis of power grids for optimized checking, planning and management
  • Noise identification and signal integrity check
  • Analysis of vibration generated by plants (basic and advanced analysis)
  • Static and dynamic electric analysis on motors and generators to identify possible differences that might lead to damage
  • Infield calibration of rotary parts to minimize the effects of vibration on structure and bearings

The benefits of predictive maintenance include increased revenues, reduction of labor costs, maintenance hours and equipment costs, enhanced safety, increased efficiency of maintenance staff, and creation of working orders with a clear definition of timelines and operating modes.

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