Assessement of current health of hard disk drives

Abstract

After investigating several of different degradation signatures that can potentially characterize aging and failure of computer hard disk drives (HDDs), we identified that reported uncorrect, hardware ECC recovered and read write rate parameters can provide good degradation signature for assessing the condition and remaining useful life of HDDs. Using these signatures as inputs, we develop a neural network model to assess the current health of a HDD. We collected extensive data by conducting experiments on 13 HDDs in an accelerated degradation mode. Experiments on 13 HDDs generated several hundreds of data points during their operating life. We used two thirds of these data points for computing the neural network parameters and the rest for evaluating the accuracy of model predictions. The results indicate that the trained neural network is able to assess the health of a HDD correctly 88 times out of 100 instances.