Summary form only given. The complete presentation was not made available for publication as part of the conference proceedings. 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 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. We also present some enhanced methods in model adaptation for increased flexibility to properly compensate system drift and shifts, such as dynamic forgetting, rule merging and splitting as well as an incremental update of the latent variable sub-space as a variant of incremental feature space transformation (accounting for dynamic changes in the influences of input variables on the output). The talk concludeS with a real-world application scenario from a (micro-fluidic) chip production site, where the timeseries based forecast models have been successfully applied; some results were presented.
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