Predictive Maintenance –
With predictive maintenance, you can design maintenance processes efficiently and thus prevent machine malfunctions. In our advanced world, intelligent processes for maintaining a machine are especially important to remain relevant as a machine builder. Transaction-Network’s platform provides you with everything you need for predictive maintenance of your machines. In addition, predictive maintenance is cleverly integrated into other processes at our company, thus ensuring optimized workflows. Take your company’s quality and productivity to a new level with improved maintenance processes.
What is Predictive Maintenance?
Predictive maintenance in industrial practice describes all predictive measures for the maintenance and repair of machines and plants. Predictive maintenance and condition monitoring go hand in hand. Data obtained through condition monitoring is intelligently evaluated with predictive maintenance as a proactive strategy. On this basis, regular maintenance is suggested before faults occur or critical components fail. Predictive maintenance helps you determine the perfect time for maintenance work on machines and systems. But only when maintenance is really necessary. This can save you valuable time and resources.
Predictive Maintenance: All advantages at a glance
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This is what predictive maintenance
looks like at Transaction-Network
Predictive maintenance is above all closely linked to the processes and data from condition monitoring. Condition monitoring saves a large amount of data as well as the status of the machine and then communicates it to the platform. By intelligently integrating other workflows and processes, predictive business processes can be triggered based on this. Predictive maintenance can consist of different stages at Transaction-Network.
Predictive maintenance is above all closely linked to the processes and data from condition monitoring. Condition monitoring saves a large amount of data as well as the status of the machine and then communicates it to the platform. By intelligently integrating other workflows and processes, predictive business processes can be triggered based on this. Predictive maintenance can consist of different stages at Transaction-Network.
Predictive Maintenance – Application Examples Level 1
In the first stage, numerous intelligent decisions can already be made based on the evaluation of data from condition monitoring:
- Plan and trigger spare parts orders based on the status of the machine
- Plan and execute the maintenance of a service technician
A concrete application example here would be the timely reordering of oil. If the data in the condition monitoring shows that a machine needs new oil after 1000 operating hours, the ordering of oil after this time can be planned in advance. By interlocking with the other modules, new oil can be reordered directly via the onlineshop and a service ticket can be triggered via the Customer Service module.
Predictive Maintenance – Application Examples of the Advanced Level
In the advanced stage, predictive maintenance is combined with an AI or neural networks. The artificial intelligence learns from the machine’s performance parameters, such as temperature or speed. As a result, it can detect and correct deficiencies at an earlier stage, before they cause problems. This enables predictive maintenance based on the current condition of the machine.
For example, the intelligent predictions of status changes can detect when a machine needs more oil under current conditions. In this case, an oil change can be scheduled earlier due to the data evaluations of the AI.
At Transaction-Network, we provide you with all the tools, capabilities and platform you need for intelligent predictive maintenance. All you need to bring is the technical knowledge of your own machines. This makes it easy for you to integrate predictive maintenance into your company, even without digital know-how. Because with our help and the collaborative set-up of the processes, you can benefit from all the advantages of predictive maintenance in the shortest possible time.