Achieving Predictive Precision: Leveraging LSTM and Pseudo Labeling for Volvo's Discovery Challenge at ECML-PKDD 2024

This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict maintenance needs for a component of Volvo trucks. We processed the training data to mirror the test set structure and app...

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Bibliographic Details
Published in:arXiv.org 2024-09
Main Authors: Metta, Carlo, Gregnanin, Marco, Papini, Andrea, Galfrè, Silvia Giulia, Fois, Andrea, Morandin, Francesco, Fantozzi, Marco, Parton, Maurizio
Format: Article
Language:English
Subjects:
Labeling
Labels
Machine learning
Performance prediction
Predictive maintenance
Online Access:Get full text
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Summary:This paper presents the second-place methodology in the Volvo Discovery Challenge at ECML-PKDD 2024, where we used Long Short-Term Memory networks and pseudo-labeling to predict maintenance needs for a component of Volvo trucks. We processed the training data to mirror the test set structure and applied a base LSTM model to label the test data iteratively. This approach refined our model's predictive capabilities and culminated in a macro-average F1-score of 0.879, demonstrating robust performance in predictive maintenance. This work provides valuable insights for applying machine learning techniques effectively in industrial settings.
ISSN:2331-8422