Hard Disk Drive Failure Prediction Method Based On A Bayesian Network

• Main Authors: Chaves, Iago C., de Paula, Manoel Rui P., Leite, Lucas G. M., Gomes, Joao Paulo P., Machado, Javam C.
• Format: Conference Proceeding
• Language: English
• Subjects: Bayes methods; Estimation; Feature extraction; Forestry; Mathematical model; Random variables; Smoothing methods
• ISSN: 2161-4407
• DOI: 10.1109/IJCNN.2018.8489097

The ability to predict failures in Hard Disk Drives (HDD) is a major objective of HDD manufacturers since avoiding unexpected failures may prevent data loss. As a consequence, failure prediction in HDDs became a topic that attracted much attention in recent years. Nowadays, most HDDs are equipped with a threshold-based monitoring system named Self-Monitoring, Analysis and Reporting Technology (SMART). The system collects several performance parameters and detects anomalies that may indicate incipient failures. Although the SMART system is very popular, it achieves failure detection rates of 3% to 10%. Moreover, SMART works as an incipient failure detection method and does not provide an estimate of the remaining life of the HDD. In this paper, we propose a failure prediction method using SMART attributes and a Bayesian Network. The proposed method, named Bayesian network based Method for Failure prediction in HDDs (BNFH) uses a subset of the SMART attributes and a set of SMART trend related attributes to provide remaining life estimates of HDDs. To demonstrate practical usefulness, this method was applied to a dataset consisting of 49,056 hard drives from Backblaze's data centers.