Seoul National University of Science and Technology Researchers Develop a Reliable AI System for SSD Failure Prediction

An AI approach helps distinguish genuine SSD failures from false failure reports in large-scale data centers

SEOUL, South Korea, Sept. 16, 2026 /PRNewswire/ -- Solid-state drives (SSDs), which store data in computers and servers, are critical to data center reliability. SSD failures can cause service disruptions, data loss and high maintenance costs, making early prediction important. SSDs continuously generate S.M.A.R.T. (Self-Monitoring, Analysis and Reporting Technology) logs containing information on errors, wear and operating conditions. Machine-learning models can analyze these logs to estimate failure risk.

However, failure reports used to train these models are not always perfectly accurate. When an abnormal event occurs, it can be difficult and time-consuming to pinpoint the exact faulty SSD, so several drives in the same rack may instead be reported as failed. As a result, healthy drives can receive failure labels in the training data, potentially misleading conventional AI models.

Now, a research team led by Assistant Professor Jaewoong Shim from the Department of Data Science, Seoul National University of Science & Technology (SEOULTECH), addressed this challenge using Multiple Instance Learning (MIL). The developed approach groups SSDs reported from the same rack on the same date, allowing the model to learn from group-level information while estimating the failure risk of individual drives. The study was conducted in collaboration with researchers from Samsung Electronics, South Korea, using real-world SSD data from an Alibaba Cloud data center. The research was made available online on July 06, 2026 and was published in Volume 219 of Computers & Industrial Engineering in September 01, 2026.

"Industrial AI has to work with the data that are actually available in the real world, and those data are not always perfectly labeled," explains Dr. Shim. "Our goal was to develop a way for AI to learn from these imperfect failure reports without assuming that every reported SSD failure is correct."

Researchers grouped related SSD data into "failure bags" using MIL. SSD sequences from drives in the same rack with failure reports on the same date were grouped together. A temporal convolutional network (TCN) then analyzed each SSD's S.M.A.R.T. data over time to estimate individual failure risk. During training, the model combined these predictions at the bag level while still producing individual SSD predictions during inference.

The researchers evaluated performance using an F1 score, which balances precision and recall. Scores range from 0 to 1, with higher scores indicating better performance. Under a 0% false-failure condition, the conventional model achieved an F1 score of 0.731. When the researchers simulated a 40% false-failure rate in the training data, its score fell to 0.261. In contrast, the mean-pooling variant of the proposed approach achieved an F1 score of 0.717 under the same 40% condition, demonstrating greater resilience to inaccurate failure labels.

The model also distinguished genuine failures from healthy SSDs incorrectly reported as failed, ranking true failures an average of 1.6 compared with 3.5 for incorrectly reported failures. Overall, the approach could help data-center operators prioritize inspections, backups, monitoring and SSD replacements, enabling them to focus resources on drives most likely to require attention.

"The idea is not limited to SSDs," said Dr. Shim. "It could also be useful for other industrial settings such as battery packs, industrial machinery and other systems where a problem can be identified within a group, but the exact component responsible is difficult to determine."

ReferenceReference
Title of original paper: Multiple Instance Learning for SSD Failure Prediction Under Customer Failure-Biased Labels
Journal: Computers & Industrial Engineering
DOI: https://doi.org/10.1016/j.cie.2026.112229 

About the institute Seoul National University of Science and Technology (SEOULTECH) 
Website: https://en.seoultech.ac.kr/

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SOURCE Seoul National University of Science and Technology