@inproceedings{38d639afb08d4424a5589218950c1341,
title = "An Interpretable Predictive Model for Early Detection of Hardware Failure",
abstract = "This paper develops an accurate yet interpretable machine learning framework for predicting field failures from time-series diagnostic data with application to datacenter hard disk drive failure prediction. Interpretable models are accountable: model reasoning can be verified by a domain expert for critical reliability tasks. We develop an attention-augmented recurrent neural network that visualizes the temporal information used to generate predictions; visualizations correlate with physical expectations. Finally, we propose a clustering-based method for discovering failure modes.",
keywords = "Failure Prediction, Hard Disk Drives, Interpretable Prediction, Machine Learning, System Reliability",
author = "Artsiom Balakir and Alan Yang and Elyse Rosenbaum",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Reliability Physics Symposium, IRPS 2020 ; Conference date: 28-04-2020 Through 30-05-2020",
year = "2020",
month = apr,
doi = "10.1109/IRPS45951.2020.9129615",
language = "English (US)",
series = "IEEE International Reliability Physics Symposium Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Reliability Physics Symposium, IRPS 2020 - Proceedings",
address = "United States",
}