A Explainability-Driven Approach to SSD Failure Detection Using LIME and SHAP Analysis

Abstract

The Solid-state drives (SSDs) have become critical components of modern computing infrastructures because of their high performance, low latency, and increasing deployment in data-intensive systems. However, SSD degradation and failure can develop through complex interactions among workload characteristics, operational conditions, device behavior, and accumulated wear, making reliable failure detection a challenging predictive task. Conventional machine-learning-based failure prediction can provide accurate classifications while remaining difficult for engineers and system administrators to interpret. This creates an important requirement for explainability, particularly when predictive decisions influence preventive maintenance, storage migration, or system availability. This paper proposes an explainability-driven framework for SSD failure detection that integrates predictive modeling with Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP). The methodology treats explainability not as a post-processing visualization but as an analytical layer connecting SSD telemetry, predictive outputs, feature contributions, and operational decisions. The framework incorporates principles derived from evolutionary optimization, intelligent search, recommender systems, large language model research, and computational materials discovery to establish a broader theoretical foundation for data-driven optimization and interpretable decision support. Particular emphasis is placed on feature-level attribution, local versus global explanations, explanation consistency, failure-risk prioritization, and the trade-off between predictive complexity and interpretability. The resulting framework provides a systematic basis for identifying influential SSD health indicators, validating model behavior, and translating failure predictions into actionable maintenance decisions. The study further identifies limitations associated with explanation stability, correlated telemetry variables, model dependence, and the absence of a standardized explanatory ground truth. The proposed approach contributes an interpretable architecture for trustworthy SSD failure detection and establishes directions for future empirical validation.