Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles
J. Zenisek, M. Affenzeller, J. Wolfartsberger, M. Silmbroth, C. Sievi, A. Huskic, H. Jodlbauer - Sliding Window Symbolic Regression for Predictive Maintenance using Model Ensembles - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 8
Predictive Maintenance (PdM) is among the trending topics
in the current Industry 4.0 movement and hence, intensively investigated.
It aims at sophisticated scheduling of maintenance, mostly in the area
of industrial production plants. The idea behind PdM is that, instead
of following xed intervals, service actions could be planned based upon
the monitored system condition in order to prevent outages, which leads
to less redundant maintenance procedures and less necessary overhauls.
In this work we will present a method to analyze a continuous stream of
data, which describes a system's condition progressively. Therefore, we
motivate the employment of symbolic regression ensemble models and
introduce a sliding-window based algorithm for their evaluation and the
detection of stable and changing system states.