Dynamic Predictive Maintenance with Self-Adaptive Evolving Forecast Models

Description

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. This is achieved i) by recursive adaptation of model parameters to permanent changes and to increase model significance and accuracy and ii) by evolution of new model components (rules) on the fly in order to account for variations in the process, which require a change in the model’s ‘non-linearity degree’. We will also present some enhanced methods in model adaptation for increased flexibility to properly compensate system drift and shifts. In the second stage, the evolved and incrementally adaptive 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.

Period	07 Apr 2021
Event title	13th Asian Conference on Intelligent Information and Database Systems
Event type	Conference
Location	ThailandShow on map