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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.
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
URL: https://doi.org/10.1002/asmb.2829
DOI: 10.1002/asmb.2829
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