Maintenance and repair are business-critical factors for the manufacturing industry. Currently, the approach of periodic maintenance dominates in companies. Machines are checked for condition and possible wear at regular time intervals. The disadvantage: such fixed maintenance windows are relatively inflexible and do not take into account the actual condition of the manufacturing equipment.


When performing periodic maintenance, checklists are worked through to ensure that critical elements of the machine are operating smoothly and that the manufacturer’s maintenance specifications are being met. If inconsistencies are found or a component shows out-of-specification wear, the affected components are replaced.


How can conventional maintenance approaches be optimized?

However, periodic maintenance offers few starting points for proactively handling unforeseen incidents. Severe wear of components or other problems that occur outside the established maintenance schedule usually go undetected and can cause lower efficiency or even machine failure.


This is where new and sustainable concepts come into play, providing more flexibility by breaking down the rigid structures of periodic maintenance. This gives companies the opportunity to intervene proactively and prevent failures before they occur. The concept of predictive maintenance in particular offers enormous potential for improving maintenance and carrying it out more efficiently, as well as increasing plant availability. The prerequisites for this are the connection of the machines to the Internet of Things (IoT) and the live data available as a result.


What is predictive maintenance?

Predictive maintenance is based on the digital twin and on Industry 4.0. With the help of sensors and cloud solutions, data on the condition of the machine or individual components is recorded, collected and evaluated. On the basis of permanent monitoring, predictive maintenance ensures that maintenance takes place at the right time and components are replaced before a defect leads to the failure of the entire machine and a production standstill. Faults and wear and tear can be identified and remedied at an early stage.


How does predictive maintenance work?

With the help of the Internet of Things, the collected data is transferred to the cloud and stored there. The information collected already provides a certain amount of information and can be used to ensure the operation of machines and systems. For predictive maintenance, it is also essential to determine the significance of a certain value for the machine and its lifecycle. Machine learning helps here by identifying correlations, anomalies and patterns from the data sets obtained. A number of different algorithms are used to train the system with a frequent number of runs based on the historical data.


Benefits of predictive maintenance

With periodic maintenance, there are fixed periods of time when a machine is taken out of operation to perform the necessary maintenance tasks. In combination with predictive maintenance, these planned downtimes can be used more efficiently. The data obtained allows conclusions to be drawn about the condition of individual components that would perhaps not be considered as part of the next scheduled maintenance. In this way, potential problems can be identified at an early stage and rectified during the downtime that was planned anyway.


With predictive maintenance, machines fail less frequently and have correspondingly higher availability. Predictive maintenance also offers advantages in the event of an unplanned downtime. On the one hand, the response time is reduced due to the permanent monitoring of the equipment. On the other hand, all historical data on the equipment is stored in the system. This can be used to start analyses of faults and thus discover the cause of the failure more quickly and minimize the unplanned downtime.


Conclusion: Combine instead of replace

Predictive maintenance is an optimal approach to meaningfully incorporate captured IoT data into maintenance. It permanently maps the conditions of individual components and enables maintenance to be carried out at the strategically best time. Components are changed neither too early nor too late, which leads to a significant cost reduction in the long run.


It is not the claim of predictive maintenance to replace periodic maintenance. Rather, it can usefully complement and extend the existing strategy to enable operators to act proactively when it comes to maintenance. The interaction of periodic maintenance and proactive concepts achieves maximum added value. In this way, the defined downtimes of periodic maintenance can be used more efficiently. The result: Unplanned downtimes are avoided and machine availability is significantly increased.


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