Authors:
Jean-Victor Autran
1
;
2
;
Véronique Kuhn
1
;
Jean-Philippe Diguet
2
;
Matthias Dubois
1
and
Cédric Buche
3
Affiliations:
1
ArianeGroup, Issac, France
;
2
IRL CROSSING, CNRS, Adelaide, Australia
;
3
ENIB, Brest, France
Keyword(s):
Data Labeling, Discretization, Predictive Maintenance, Data Preprocessing.
Abstract:
In machine learning, effective data preprocessing, particularly in the context of predictive maintenance, is a key to achieving accurate predictions. Predictive maintenance datasets commonly exhibit binary health states, offering limited insights into transitional phases between optimal and failure states. This work introduces an approach to label data derived from intricate electronic systems based on unsupervised discretization techniques. The proposed method uses data distribution patterns and predefined failure thresholds to discern the overall health of a system. By adopting this approach, the model achieves a nuanced classification that not only distinguishes between healthy and failure states but also incorporates multiple transitional states. These states act as intermediary phases in the system’s progression toward potential failure, enhancing the granularity of predictive maintenance assessments. The primary objective of this methodology is to increase anomaly detection cap
abilities within electronic systems. Through the utilization of unsupervised discretization, the model ensures a data-driven approach to system monitoring and health evaluation. The inclusion of multiple transitional states in the labeling process facilitates a more precise predictive maintenance framework, enabling informed decision-making in maintenance strategies. This article contributes to advancing the effectiveness of predictive maintenance applications by addressing the limitations associated with binary labeling, ultimately encouraging a more nuanced and accurate understanding of system health.
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