Failure rates of Hard Disk Drives (HDDs) are high and often due to a variety of different conditions. Thus, there is increasing demand for technologies dedicated to anticipating possible causes of failure, so to allow for preventive maintenance operations. In this paper, we propose a framework to predict HDD health status according to a long short-term memory (LSTM) model. We also employ eXplainable Artificial Intelligence (XAI) tools, to provide effective explanations of the model decisions, thus making the final results more useful to human decision-making processes. We extensively evaluate our approach on standard data-sets, proving its feasibility for real world applications.
CITATION STYLE
Galli, A., Moscato, V., Sperlí, G., & Santo, A. D. (2020). An explainable artificial intelligence methodology for hard disk fault prediction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12391 LNCS, pp. 403–413). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-59003-1_26
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