Predictive maintenance relies on real-time monitoring and diagnosis of system components, and process and production chains. The primary strategy is to take action when items or parts show certain behaviors that usually result in machine failure, reduced performance or a downtrend in product quality.

In the first stage, it is thus of utmost importance to recognize potentially arising problems as early as possible. Therefore, a core component in predictive maintenance systems is the usage of techniques from the fields of forecasting and prognostics, which can either rely on process parameter settings (static case) or process values recorded over time (dynamic case). We will focus on the latter and demonstrate a robust learning procedure of time-series based forecast models, which can deal with very high-dimensional batch process modeling settings. Furthermore, our approach allows the forecast models to be on-line updated over time and on the fly whenever required due to intrinsic system dynamics (such as, e.g. varying product types, charges, settings, environmental influences) => leading to the paradigm of self-adaptive forecast models.

In the second stage, the forecast models can be used as surrogates in a fully automatized optimization procedure in order to prevent operator’s intervention and time-intensive manual reactions to predicted downtrends. The assumption is that there are some “control wheels”, usually machine parameter settings which are able to change the behavior of the production process in order to meet the quality standards. Such settings may have indeed been optimized before, but may not take into account dynamically changing factors during production. In other cases, such settings could not be optimized before at all (as requiring time-intensive design of experiments cycles) such that often a default parametrization is used which is suboptimal for the final product quality.