Publication Date
Spring 2024
Degree Type
Thesis
Degree Name
Master of Science (MS)
Department
Computer Science
Advisor
Saptarshi Sengupta; Genya Ishigaki; Viji Krishnamurthy
Abstract
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.
Recommended Citation
Mohapatra, Rohan, "Deep-Learning Approaches to Predict Remaining Useful Life of Hard Disks" (2024). Master's Theses. 5520.
DOI: https://doi.org/10.31979/etd.8z52-gsyb
https://scholarworks.sjsu.edu/etd_theses/5520
