A combined Bayesian network method for predicting drive failure times from SMART attributes
Shuai Pang, Yuhan Jia, Rebecca J. Stones, Gang Wang, Xiaoguang Liu · 2016
Statistical and machine learning methods have been proposed to predict hard drive failure based on SMART attributes, and many achieve good performance. However, these models do not give a good indication as to when a drive will fail, only predicting that it will fail. To this end, we propose a new notion of a drive's health degree based on the remaining working time of hard drive before actual failure occurs. An ensemble learning method is implemented to predict these health degrees: four popular individual classifiers are individually trained and used in a Combined Bayesian Network (CBN). Experiments show that the CBN model can give a health assessment under the proposed definition where drives are predicted to fail no later than their actual failure time 70% or more of the time, while maintaining prediction performance standards at least approximately as good as the individual classifiers.