Continual Learning for Predictive Maintenance: Overview and Challenges

Continual Learning for Predictive Maintenance: Overview and Challenges. Hurtado, J., Salvati, D., Semola, R., Bosio, M., & Lomonaco, V. January, 2023. arXiv:2301.12467 [cs]

Paper doi abstract bibtex

Machine learning techniques have become one of the main propellers for solving many engineering problems effectively and efficiently. In Predictive Maintenance, for instance, Data-Driven methods have been used to improve predictions of when maintenance is needed on different machines and operative contexts. However, one of the limitations of these methods is that they are trained on a fixed distribution that does not change over time, which seldom happens in real-world applications. When internal or external factors alter the data distribution, the model performance may decrease or even fail unpredictably, resulting in severe consequences for machine maintenance. Continual Learning methods propose ways of adapting prediction models and incorporating new knowledge after deployment. The main objective of these methods is to avoid the plasticity-stability dilemma by updating the parametric model while not forgetting previously learned tasks. In this work, we present the current state of the art in applying Continual Learning to Predictive Maintenance, with an extensive review of both disciplines. We first introduce the two research themes independently, then discuss the current intersection of Continual Learning and Predictive Maintenance. Finally, we discuss the main research directions and conclusions.

@misc{hurtado_continual_2023,
	title = {Continual {Learning} for {Predictive} {Maintenance}: {Overview} and {Challenges}},
	shorttitle = {Continual {Learning} for {Predictive} {Maintenance}},
	url = {http://arxiv.org/abs/2301.12467},
	doi = {10.48550/arXiv.2301.12467},
	abstract = {Machine learning techniques have become one of the main propellers for solving many engineering problems effectively and efficiently. In Predictive Maintenance, for instance, Data-Driven methods have been used to improve predictions of when maintenance is needed on different machines and operative contexts. However, one of the limitations of these methods is that they are trained on a fixed distribution that does not change over time, which seldom happens in real-world applications. When internal or external factors alter the data distribution, the model performance may decrease or even fail unpredictably, resulting in severe consequences for machine maintenance. Continual Learning methods propose ways of adapting prediction models and incorporating new knowledge after deployment. The main objective of these methods is to avoid the plasticity-stability dilemma by updating the parametric model while not forgetting previously learned tasks. In this work, we present the current state of the art in applying Continual Learning to Predictive Maintenance, with an extensive review of both disciplines. We first introduce the two research themes independently, then discuss the current intersection of Continual Learning and Predictive Maintenance. Finally, we discuss the main research directions and conclusions.},
	urldate = {2023-06-03},
	publisher = {arXiv},
	author = {Hurtado, Julio and Salvati, Dario and Semola, Rudy and Bosio, Mattia and Lomonaco, Vincenzo},
	month = jan,
	year = {2023},
	note = {arXiv:2301.12467 [cs]},
	keywords = {Computer Science - Machine Learning},
}

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