Predictive maintenance is one of the best-known applications in the age of Industry 4.0. Thanks to predictive maintenance, a wide variety of data can be obtained from industrial machines and thus, for example, plants can be proactively maintained and optimized. This condition-based maintenance of machines aims to avoid unexpected failures and requires a regular review of the actual condition, efficiency and other relevant indicators.
The use in the business context in companies is very helpful and has a considerable influence on the current business. A targeted data analysis and evaluation, e.g. of a production plant, influences, among other things, the forecasting process for revenue and sales planning, as well as the derivation of further production planning based on this. Companies have recognized that the benefit of predictive maintenance is not only for the maintenance of machines, but influences the entire value chain.
When used correctly and efficiently, predictive maintenance can deliver a wide range of benefits - for both the user and the manufacturer. The most important benefits are:
- Improved profitability: firstly, the use of predictive maintenance can reduce the downtime of machines and equipment, as well as reduce costs incurred for unplanned downtime. Secondly, continuous maintenance of machines and systems can increase their service life.
- Optimal maintenance time: The perfect time for maintenance can be precisely determined with predictive maintenance through the permanent evaluation of data. In addition, maintenance can thus be smoothly integrated into the production process.
- Improving machine performance: Through the ongoing analysis of data, it is possible to increase the performance of the machine and achieve higher productivity in the long term.
The challenge of predictive maintenance is: Companies have to collect enormous amounts of data (big data) in order to obtain a reliable statement about the condition of systems and malfunctions. Because the collected data about temperature, speed, vibrations, humidity, etc. have different formats, the evaluation of this data often becomes a hurdle.