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Improve OEE and product quality with predictive maintenance and condition monitoring

If machine manufacturers use predictive maintenance and condition monitoring, they can generate interesting advantages for themselves and their customers: Predictive maintenance and condition monitoring have a direct positive effect on increasing the quality of the output of machines, because all components of a machine must fulfil their respective functions faultlessly. This makes it possible to work...



Productivity, profitability and effectiveness are the top priorities. Machine manufacturers can contribute enormously to this with the help of predictive maintenance and condition monitoring and thus create advantages: If they determine the condition of machine components during operation, these can be improved within the scope of such permanent monitoring. At the same time, this reduces unplanned downtimes for the customer or the machine operator, or even prevents them altogether. This gives machine manufacturers and customers a real lever to avoid losses in Overall Equipment Effectiveness (OEE).


Sustainability, environmental protection and budget savings


Predictive maintenance and condition monitoring also reduce the time required for maintenance, because the time at which wear parts are replaced can be chosen much more precisely. Spare parts simply have to be replaced when the end of the remaining service life has actually been reached and not when the maintenance schedule prescribes replacement. This saves budget, the spare parts stock is much more efficient and less effort is required for maintenance. The fact that functioning components are no longer disposed of before their time pays off in terms of sustainability and environmental protection.


Predictive maintenance and condition monitoring also ensure the quality of the machine output.


Since components that are heavily used and already near the end of their product life cycle are no longer at full capacity, it makes sense to identify them. Because - even if the machines are still running with these components - there is usually an increasing amount of scrap. By recording and evaluating the data of wear components through predictive maintenance and condition monitoring, it can be determined which components are responsible for this and where possibly readjustments can be made within the machine.


Good ideas that speak in favour of relying on predictive maintenance and condition monitoring.


More information at: www.senzoro.ai



Author: DI. Mag. Markus Loinig

Email: markus@senzoro.com

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