Interpretable predictive maintenance for hard drives
نویسندگان
چکیده
Existing machine learning approaches for data-driven predictive maintenance are usually black boxes that claim high power yet cannot be understood by humans. This limits the ability of humans to use these models derive insights and understanding underlying failure mechanisms, also degree confidence can placed in such a system perform well on future data. We consider task predicting hard drive data center using recent algorithms interpretable learning. demonstrate methods provide meaningful about short- long-term health, while maintaining performance. show analyses still deliver useful even when limited historical is available, enabling their situations where collection has only recently begun.
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ژورنال
عنوان ژورنال: Machine learning with applications
سال: 2021
ISSN: ['2666-8270']
DOI: https://doi.org/10.1016/j.mlwa.2021.100042