In simple terms, predictive maintenance is essentially based on three pillars:
- Permanent or periodic collection and storage of machine and process data
- (Automatic) analysis and evaluation of data
- Calculation of probabilities of occurrence of certain events
To evaluate the actual state of a device, various inspection methods are used, for example, by means of infrared, vibration analysis, acoustic or sound level measurements. For example, temperatures, rotational speeds, noises or running times are determined. Predictive maintenance therefore refers to the actual condition of the machines, not - as with preventive maintenance - to the average or expected service life. This measured data is then linked to other information, such as machine failures, malfunctions or repairs. This allows conclusions to be drawn about expected future maintenance requirements.
A huge amount of data is needed to make the calculations as accurate as possible. Predictive maintenance procedures are therefore particularly worthwhile for companies that use many machines of the same type or for manufacturers of these machines who want to use predictive maintenance not only for their own machines but also for those they sell.