Abstract
Predictive maintenance offers the possibility to prognosticate the remaining time until a maintenance action of a machine has to be scheduled. Unfortunately, current predictive maintenance solutions are only suitable for very specific use cases like reliability predictions based on vibration monitoring. Furthermore, they do not consider the fact that machines may deteriorate non-uniformly, depending on external influences (e.g., the work piece material in a milling machine or the changing fruit acid concentration in a bottling plant). In this paper two concepts for a generic predictive maintenance solution which also considers non-uniformly aging behaviour are introduced. The first concept is based on system models representing the health state of a technical system. As these models are usually statically (viz. without a timely dimension) their coefficients are determined periodically and the resulting time series is used as aging indicator. The second concept focuses on external influences (contexts) which change the behaviour of the previous mentioned aging indicators in order to increase the accuracy of reliability predictions. Therefore, context-depended time series models are determined and used to predict machine reliability. Both concepts were evaluated on data of an air ventilation system. Thereby, it could be shown that they are suitable to determine aging indicators in a generic way and to incorporate external influences in the reliability prediction. Through this, the quality of reliability predictions can be significantly increased. In reality this leads to a more accurate scheduling of maintenance actions. Furthermore, the generic character of the solutions makes the concepts suitable for a wide range of aging processes.
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