Predictive Maintenance: Efficiency in industry 4.0

Application examples of Predictive Maintenance

A very good example of the use of Predictive Maintenance can be found in many vehicles. Thanks to the extensive data collection by many different sensors it is possible to reduce expensive breakdowns and repairs of a vehicle to a minimum. For this purpose, the sensors record a wide variety of data in the engine and the chassis and compare them both with the optimum and with the data history. This means that any damage that may occur can be detected at an early stage and reported to the driver by the software. Even further are vehicles with networked telemetry, which are able to report this data directly to the workshop or the manufacturer of the vehicle. In such a case, not only the vehicle owner can be directly informed via such a system, but also the responsible authorised workshop. This means that it can stock up on the required spare parts at an early stage and thus reduce the repair time to a minimum.

Case Study

Audi reduces prototyping lead time by using 3D printing for tail light covers

Predictive Maintenance is also becoming increasingly popular in the industry. Thanks to the sensors also installed here, vibrations, temperatures and noise of a machine, for example, can be permanently monitored. Even the smallest deviations are thus registered and can, for example, indicate the failure of a bearing at an early stage. In such a case, the bearing can be replaced in time without further delays. Thanks to Predictive Maintenance, it is already known which component is to be replaced in which area of the machine, which also minimizes maintenance time. The downtimes of the entire machine and also the working time of the service technicians can thus be reduced to a minimum.

Spotlight

Mitsubishi Electric’s technological revolution

Some of the systems maintained by Predictive Maintenance in the industry include wind turbines. In this way, turbine downtimes can be reduced to a minimum through Predictive Maintenance. Thanks to intelligent mathematical algorithms, the vibration analysis of the various components can be optimally adjusted so that reliable predictions about failure probabilities of individual components are possible. If these forecasts are combined with the prevailing wind conditions and the planned downtimes of such a turbine, the replacement of the endangered components can be carried out early and thus without considerable effort. This saves time and costs and also prevents a longer and unplanned downtime of the entire system.

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