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Cassantec Knows When Machines Fail

Hannover Messe Special Exhibit on Predictive Maintenance 4.0

Asset operators are relying increasingly on Predictive Maintenance. By using data acquired from condition monitoring and by applying innovative software solutions, the optimal timing for maintenance can be determined. Hence, for the second time, Hannover Messe is giving the topic of "Predictive Maintenance" its own separate section, in which exhibitors address how to avoid downtime, optimise energy consumption and reduce maintenance costs. Swiss company Cassantec AG with its daughter company in Berlin will also present its prognostic solutions for equipment malfunctions. Cassantec Prognostics, as the solution is called, provides the information on timing needed for Predictive Maintenance. With this tool, the company prognosticates when component malfunctions will occur. At the exhibition stand, Cassantec presents several different exhibits to demonstrate to visitors how the forecasts are applied as the “killer app” for Predictive Maintenance.

Cassantec is the “killer app” for Predictive Maintenance

Moritz von Plate, CEO of Cassantec AG

Whether it be in power stations, in refineries or in the transport sector, many industries are working on “Predictive Maintenance” initiatives. Digital networking of machines makes it possible to collect data on a continuous basis and to process this in a logical manner using software solutions such as Cassantec Prognostics. With the aid of condition-based availability forecasts, users optimise their asset management and have at their disposal risk profiles for future malfunctions. “Appropriately adapted maintenance plans ensure that unnecessary costs are pared down. For example, replacement of parts does not take place in accordance with a fixed, regular cycle but rather when their condition renders it necessary. In this way users of Cassantec Prognostics avoid unnecessary downtime of their plants”, explains Moritz von Plate, CEO of Cassantec AG. He goes on to say: “Active management of the Remaining Useful Life of the plant is a big economic advantage for companies. Our software uses a traffic-light system to show the user at what point in time a malfunction occurs.” In this context, prognostic reports provide a forecast horizon of months, sometimes even years.

Predictive Maintenance is key

A targeted implementation of Predictive Maintenance generates a huge potential for savings on scheduled repairs in comparison with unscheduled events and on maintenance costs in general. This yields shorter downtimes and maintenance times, falling service costs, faster manufacturing flows and greater productivity. “The potential for development has not by any means been exhausted. In addition, machine learning contributes to continued improvement“, says Mr von Plate. For those interested, Hall 19 provides individual and joint exhibits, showcases and discussions, all relating to the topic of Predictive Maintenance.

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