Transparent SSD Failure Prediction Through Interpretable Artificial Intelligence Techniques

Abstract

Solid-state drives (SSDs) have become critical storage components in computing infrastructures, cloud platforms, enterprise systems, and intelligent devices, making reliable failure prediction an important component of predictive maintenance and system dependability. Conventional machine-learning models can identify complex relationships among storage-health indicators, workload characteristics, error statistics, thermal conditions, and operational behavior, but their limited interpretability can restrict their practical adoption. This research develops a conceptual framework for transparent SSD failure prediction through interpretable artificial intelligence (AI), emphasizing the integration of predictive modeling with post-hoc explanation mechanisms such as Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The methodological foundation is positioned within the broader development of advanced AI systems, including transformer-based, multimodal, reasoning-oriented, and neural architectures represented in the supplied literature. The framework separates prediction from explanation while establishing an evidence-oriented pathway through which individual SSD-health indicators can be associated with predicted failure risk. The analysis indicates that transparency should not be treated as an auxiliary visualization layer but as an integral property of the predictive-maintenance pipeline. The study further identifies important trade-offs between predictive complexity, explanation fidelity, computational overhead, and operational usability. By incorporating interpretable AI principles into SSD failure prediction, the proposed approach provides a structured basis for trustworthy maintenance decisions while acknowledging the limitations associated with model-agnostic explanations, distribution shifts, feature correlations, and the absence of universally representative SSD failure datasets.