University of Illinois Urbana-Champaign

Causal inference for early detectection of hardware failure

Yang, Alan

Content Files
ECE499-Sp2018-yang.pdf

Permalink

https://hdl.handle.net/2142/100049

Description

Title
Causal inference for early detectection of hardware failure
Author(s)
Yang, Alan
Contributor(s)
Rosenbaum, Elyse
Issue Date
2018-05
Keyword(s)
  • hardware failure detection
  • predicting hard disk drive failures
  • information theoretic measures
Date of Ingest
2018-05-25T17:04:11Z
Abstract
Many modern hardware systems are equipped with sensors which record time-series diagnostic data. These sensors enable data-driven failure prediction that can reduce the need for component redundancy and lengthen lifetime specifications, by allowing for identification and proactive replacement of a soon-to-fail component. In this work, we develop a causal inference framework for predicting data center hard disk drive failures using multivariate time series recordings of temperature, read error rate, and other attributes. Information-theoretic measures are developed to quantify relationships between sensor variables, select prognostic features, and train a predictor. Finally, a recurrent neural network demonstrating high predictive accuracy and a low false alarm rate is developed, using field data collected from an operating data center.
Type of Resource
text
Genre of Resource
Other
Language
en
Permalink
http://hdl.handle.net/2142/100049

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