• ruhlaSmart Predictive Maintenance

    Predictive maintenance: for 
    efficient maintenance strategies

    Predictive maintenance

    Optimise maintenance processes and reduce costs

    When machines and systems require maintenance, this often results in long downtimes and high costs. However, regular maintenance is important to avoid expensive repairs and unplanned downtime. The problem is that most companies work with fixed maintenance schedules. Here, the work is carried out at fixed intervals and is little or not at all adapted to the actual maintenance requirements of the machines. As a result, a lot of superfluous maintenance work is carried out. This results in unnecessary costs due to downtime and labour costs. The solution: predictive maintenance - demand-orientated maintenance planning based on machine learning, artificial intelligence and smart tools.

    What is predictive maintenance and how does it work?

    Predictive maintenance refers to the forward-looking planning of maintenance measures. It is based on comprehensive data analyses that evaluate the machine and system status in real time. At the same time, smart tools, artificial intelligence and the like calculate the optimum maintenance time based on prediction models. This reduces unnecessary downtime, saves costs and supports the longevity of the systems. We would be happy to support you in implementing predictive maintenance with AI in your company - for example with the integration of the ruhlamat MachineHub.

    Predictive maintenance at a glance

    Predictive maintenance at a glance

    Predictive maintenance - your benefits

    System Availability
    Increased system availability

    Thanks to real-time monitoring and analysis, failures can be prevented before they occur. This reduces downtimes and improves the overall availability of the systems.

    Optimized Maintenance
    Optimized maintenance planning

    Thanks to predictive maintenance, companies can plan maintenance work more individually. This saves time and money because maintenance is only carried out when it is actually required.

    Evaluation
    Extended service life of systems

    Demand-orientated maintenance planning reduces the risk of system problems. At the same time, it supports the longevity of the machines and ultimately increases investment security.

    Cost savings
    Reduced maintenance costs

    As maintenance work is only carried out when it is actually necessary, unnecessary downtimes and work assignments are avoided. This reduces maintenance costs and prevents expensive emergency repairs and unplanned downtime.

    Quality Standars
    Increasing safety

    Permanent real-time monitoring also uncovers potential safety risks. This helps to prevent accidents and breakdowns. Employees and systems are better protected. Compliance with safety standards is fully automated.

    quality
    Improving product quality

    By recognising potential system problems before they arise, predictive maintenance also ensures the consistent quality of production processes. This reduces reworking costs and increases customer satisfaction.

    FAQs Predictive Maintenance

    What kind of data is needed for predictive maintenance?

    Various types of data are used for predictive maintenance, including sensor data, operating data, maintenance histories and external data such as room temperature and humidity. This data is used to train models that can predict future maintenance needs.

    How accurate are Predictive Maintenance's predictions?

    The accuracy of predictions depends on various factors, including the quality of the data, the complexity of the equipment and the effectiveness of the models used. However, very accurate predictions can be achieved through continuous training and improvement.

    How can my company benefit from predictive maintenance?

    By switching to predictive maintenance, your company can not only increase your maintenance efficiency, but also improve your competitiveness by reducing downtime, lowering costs and increasing plant availability.

    Get in touch with us

    For more information about our predictive maintenance solutions and how they can support your business, please contact us for a personalized consultation.

    Richard Stegmann

    Manager Digital Solutions
    Richard Stegmann