Applications of Active Learning in Predictive Maintenance

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Published Sep 4, 2023
Navid Zaman You Jung Jun Yan Li Daniel Chan

Abstract

Nowadays, the common choice in maintenance strategies is predictive maintenance (PdM), deprecating the corrective and preventive kinds. Even with various machine learning techniques to get advanced predictive models to achieve PdM, difficulties remain in the data acquisition process. While there is a plethora of unlabeled data from sensors, most of those available techniques can only process labeled data, i.e, supervised learning. To combat the fact that the availability of the labeled
data is limited, this paper proposes the use of Active Learning to label and annotate the informative instances while minimizing overall processing time. This approach maintains high performance and decreases the number of labeled instances, with support from experimental results and a discussion of the applicability of this method.

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Keywords

active learning, predictive maintenance, mission critical, machine learning

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Section
Special Session Papers