عنوان انگلیسی مقاله:
Machine learning based concept drift detection for predictive maintenance
ترجمه فارسی عنوان مقاله:
مفهوم یادگیری ماشین مبتنی بر تشخیص رانش برای تعمیر و نگهداری پیشگویانه
Sciencedirect - Elsevier - Computers & Industrial Engineering, 137 (2019) 106031: doi:10:1016/j:cie:2019:106031
Jan Zeniseka,b,⁎, Florian Holzingera, Michael Affenzellera,b
In this work we present a machine learning based approach for detecting drifting behavior – so-called concept
drifts – in continuous data streams. The motivation for this contribution originates from the currently intensively
investigated topic Predictive Maintenance (PdM), which refers to a proactive way of triggering servicing actions
for industrial machinery. The aim of this maintenance strategy is to identify wear and tear, and consequent
malfunctioning by analyzing condition monitoring data, recorded by sensor equipped machinery, in real-time.
Recent developments in this area have shown potential to save time and material by preventing breakdowns and
improving the overall predictability of industrial processes. However, due to the lack of high quality monitoring
data and only little experience concerning the applicability of analysis methods, real-world implementations of
Predictive Maintenance are still rare. Within this contribution, we present a method, to detect concept drift in
data streams as potential indication for defective system behavior and depict initial tests on synthetic data sets.
Further on, we present a real-world case study with industrial radial fans and discuss promising results gained
from applying the detailed approach in this scope.
Keywords: Predictive maintenance | Machine learning | Concept drift detection | Time series regression | Industrial radial fans