Explainable time series features for hard disk drive failure prediction

Reliable data storage is crucial for industry digitalization and cloud infrastructure. To prevent data loss and improve maintenance efficiency in data centers, timely replacement of Hard Disk Drives (HDDs) is critical. HDDs are equipped with Self-Monitoring, Analysis, and Reporting Technology (SMART) to track key performance indicators with empirically established thresholds. In recent years, various machine learning models have utilized SMART time series data for early HDD failure prediction. However, decision-makers need greater transparency and explainability to trust and implement these data-driven models. In this work, we proposed a framework that extracts explainable features from the SMART time series and visualizes the feature impact on short-term HDD failure prediction using SHapley Additive exPlanations (SHAP) analysis. We trained an eXtreme Gradient Boosting (XGBoost) model with information-rich features and evaluated the failure detection rate and false alarm rate. We demonstrated the effectiveness of the proposed approach on Backblaze data for Quarter 1 and Quarter 2 of 2022. The model provided a 74.7% failure detection rate with only a 0.73% false alarm rate on the test data from Quarter 3 of 2022, outperforming an existing explainable model benchmark of a 54.68% failure detection rate and an 11.85% false alarm rate. In addition, the sensitivity analysis optimizes the signal length and the lead time to improve prediction accuracy and inform predictive maintenance policies. The results demonstrate the potential of the proposed framework for effective HDD failure prediction with explainable features. The proposed framework is also applicable to other sensor-based industrial equipment monitoring applications.