Causal inference for early detectection of hardware failure

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.Ope