Rotary Screw Compressors Case Study

predictive IoT


A prospect company required better uptime and predictive prescriptive condition based maintenance for generating compressed air as a service.


A real-time predictive IoT model was used to address these needs on a compact platform. This platform enabled the company to read equipment sensor and set point parameter data while combining this data with client provided business rules. The solution was fully customized to their specific requirements.


The real-time solution provided maintenance insights more rapidly, reduced maintenance site visits as diagnostics were performed by the solution, improved service level agreements, and generated vastly better parts and labor demand and scheduling predictions. The predictive IoT solution enabled new business offerings previously thought impossible.


The three basic techniques for implementing predictive maintenance are condition monitoring, machine learning and simulation. RRAMAC uses the simulation approach to predictive maintenance which leverages human knowledge of cause and effect relationships which apply to the specific machine or application. These relationships are applied to simulation software to predict failures.

RRAMAC has worked with industry experts to provide predictive maintenance solutions for over a decade, covering a broad range of industries and applications. These systems can be hosted on the EdgeScout by RRAMAC cloud servers or configured as part of an on-premise hosted solution. The simulation software runs on servers, on edge nodes, or a combination of both, depending on the speed of the application. To learn more about increasing machine performance and other IoT solutions provided by RRAMAC, contact us today.