Hard drive failure prediction using non-parametric statistical methods

Abstract

We present a case study of a difficult real-world pattern recognition problem: predicting hard drive failure using attributes monitored internally by individual drives. We compare the performance of support vector machines (SVMs), unsupervised clustering, and non-parametric sta- tistical tests (rank-sum and reverse arrangements). Some- what surprisingly, the rank-sum method outperformed the other methods, including SVMs. We also show the utility of using non-parametric tests for feature set selection.