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Examining the impact of critical attributes on hard drive failure times: Multi-state models for left-truncated and right-censored semi-competing risks data

Lookup NU author(s): Dr Jordan Oakley, Dr Matthew ForshawORCiD, Dr Pete PhilipsonORCiD, Dr Kevin Wilson



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2023 The Authors. Applied Stochastic Models in Business and Industry published by John Wiley & Sons Ltd. The ability to predict failures in hard disk drives (HDDs) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss, improve service reliability, and reduce data center downtime. Most HDDs are equipped with a threshold-based monitoring system named self-monitoring, analysis and reporting technology (SMART). The system collects several performance metrics, called SMART attributes, and detects anomalies that may indicate incipient failures. SMART works as a nascent failure detection method and does not estimate the HDDs' remaining useful life. We define critical attributes and critical states for hard drives using SMART attributes and fit multi-state models to the resulting semi-competing risks data. The multi-state models provide a coherent and novel way to model the failure time of a hard drive and allow us to examine the impact of critical attributes on the failure time of a hard drive. We derive dynamic predictions of conditional survival probabilities, which are adaptive to the state of the drive. Using a dataset of HDDs equipped with SMART, we find that drives are more likely to fail after entering critical states. We evaluate the predictive accuracy of the proposed models with a case study of HDDs equipped with SMART, using the time-dependent area under the receiver operating characteristic curve (AUC) and the expected prediction error (PE). The results suggest that accounting for changes in the critical attributes improves the accuracy of dynamic predictions.

Publication metadata

Author(s): Oakley JL, Forshaw M, Philipson P, Wilson KJ

Publication type: Article

Publication status: Published

Journal: Applied Stochastic Models in Business and Industry

Year: 2023

Pages: Epub ahead of print

Online publication date: 03/12/2023

Acceptance date: 05/10/2023

Date deposited: 17/10/2023

ISSN (print): 1524-1904

ISSN (electronic): 1526-4025

Publisher: John Wiley and Sons Ltd


DOI: 10.1002/asmb.2829


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