Optimization of bi-directional gated loop cell based on multi-head attention mechanism for SSD health state classification model

TL;DR

This paper introduces a hybrid BiGRU-MHA model with multi-head attention for SSD health classification, achieving high accuracy and robustness, and addressing traditional model generalization issues for industrial storage system maintenance.

Contribution

The study proposes a novel hybrid BiGRU-MHA model that combines temporal feature extraction and key information focusing for improved SSD health prediction.

Findings

Achieved 92.70% training accuracy and 92.44% test accuracy.

ROC AUC of 0.94 indicating strong classification performance.

Model demonstrates excellent generalization and practical value for industrial maintenance.

Abstract

Aiming at the critical role of SSD health state prediction in data reliability assurance, this study proposes a hybrid BiGRU-MHA model that incorporates a multi-head attention mechanism to enhance the accuracy and stability of storage device health classification. The model innovatively integrates temporal feature extraction and key information focusing capabilities. Specifically, it leverages the bidirectional timing modeling advantages of the BiGRU network to capture both forward and backward dependencies of SSD degradation features. Simultaneously, the multi-head attention mechanism dynamically assigns feature weights, improving the model's sensitivity to critical health indicators. Experimental results show that the proposed model achieves classification accuracies of 92.70% on the training set and 92.44% on the test set, with a minimal performance gap of only 0.26%, demonstrating excellent generalization ability. Further analysis using the receiver operating characteristic (ROC) curve shows an area under the curve (AUC) of 0.94 on the test set, confirming the model's robust binary classification performance. This work not only presents a new technical approach for SSD health prediction but also addresses the generalization bottleneck of traditional models, offering a verifiable method with practical value for preventive maintenance of industrial-grade storage systems. The results show the model can significantly reduce data loss risks by providing early failure warnings and help optimize maintenance costs, supporting intelligent decision-making in building reliable storage systems for cloud computing data centers and edge storage environments.