Remaining Useful Life Estimation of Hard Disk Drives using Bidirectional LSTM Networks

TL;DR

This paper presents a Bidirectional LSTM approach for early prediction of hard disk drive failures, achieving high accuracy and low error rates, thereby enabling proactive maintenance in data centers.

Contribution

The study introduces a novel data-driven method using Bidirectional LSTM for HDD failure prediction, outperforming traditional models in accuracy and early warning capabilities.

Findings

96.4% accuracy in failure prediction 60 days in advance

Mean absolute error of 0.12 for long-term failure prediction

Superiority over vanilla LSTM and Random Forest models

Abstract

Physical and cloud storage services are well-served by functioning and reliable high-volume storage systems. Recent observations point to hard disk reliability as one of the most pressing reliability issues in data centers containing massive volumes of storage devices such as HDDs. In this regard, early detection of impending failure at the disk level aids in reducing system downtime and reduces operational loss making proactive health monitoring a priority for AIOps in such settings. In this work, we introduce methods of extracting meaningful attributes associated with operational failure and of pre-processing the highly imbalanced health statistics data for subsequent prediction tasks using data-driven approaches. We use a Bidirectional LSTM with a multi-day look back period to learn the temporal progression of health indicators and baseline them against vanilla LSTM and Random Forest models to come up with several key metrics that establish the usefulness of and superiority of our model under some tightly defined operational constraints. For example, using a 15 day look back period, our approach can predict the occurrence of disk failure with an accuracy of 96.4% considering test data 60 days before failure. This helps to alert operations maintenance well in-advance about potential mitigation needs. In addition, our model reports a mean absolute error of 0.12 for predicting failure up to 60 days in advance, placing it among the state-of-the-art in recent literature.