基于机器学习与规则推理的SSD故障预测方法研究及对比分析OA

With the rapid evolution of cloud computing,big data,and artificial intelligence applications,the scale of data centers continues to expand,and the reliability of storage systems has become a critical factor affecting their stable op-eration and service availability.As a key component of data center storage systems,solid-state drives(SSDs)are widely de-ployed in the core storage layers of data centers owing to their advantages of high throughput,low latency,and low power consumption.However,under large-scale and long-term operating conditions,SSD failures are characterized by strong sud-denness and complex evolution patterns,posing severe challenges to service continuity and data security.To enhance the ac-curacy and practicality of failure prediction,this paper investigates a machine learning prediction methodology based on classification models and feature engineering,alongside a rule-based reasoning prediction approach utilizing an explicit rule engine and dynamic feature compensation.The machine learning methodology,through multi-stage feature engineering and ensemble learning,achieves a macro-average F1-score of 0.968 under complete data conditions;however,its"black-box"na-ture somewhat limits its industrial applicability.In contrast,the rule-based reasoning approach constructs an explicit rule en-gine integrating multiple algorithms and introduces a dynamic feature compensation mechanism based on SHAP(SHapley Additive exPlanations)values.This method attains an accuracy of 0.988 with complete data and maintains an accuracy of 0.941 under extreme conditions with eight missing features,demonstrating strong robustness.Comparative analysis of ex-perimental results indicates that the machine learning methodology excels in prediction accuracy with complete data,while the rule-based reasoning approach offers superior interpretability,real-time performance,and adaptability to missing data.This paper further explores potential pathways for integrating these two methodologies,providing theoretical support and practical references for constructing next-generation intelligent operation and maintenance systems that possess both percep-tual capability and transparent reasoning.