Publication Date

Spring 2024

Degree Type

Master's Project

Degree Name

Master of Science in Computer Science (MSCS)


Computer Science

First Advisor

Saptarshi Sengupta

Second Advisor

Genya Ishigaki

Third Advisor

Viji Krishnamurthy


Deep learning, remaining life of hard disk


On a daily basis, data centers process huge volumes of data using inexpensive hard disks. Data stored in these disks serve a range of critical functional needs from financial, and healthcare to aerospace. As such, premature disk failure and consequent loss of data can be catastrophic. To mitigate the risk of failures, cloud storage providers perform condition-based monitoring and replace hard disks before they fail. By estimating the remaining useful life (RUL) of hard disk drives, one can predict the time-to-failure of a particular device and replace it at the right time, ensuring maximum utilization whilst reducing operational costs. We analyze 10-years worth of data across manufacturers and understand failure trends. In this work, we aim to look at several deep learning architectures ranging for LSTMs to Transformers to predict the RUL of hard disk for better predictive maintenance.

Available for download on Thursday, May 22, 2025