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In a large-scale data center environment, the various brands and models of drives serve diverse applications with different input\/output workload patterns, and non-ignorable differences exist in each type of drive failures, which make this mechanism much challenging. Although many efforts are devoted to this mechanism, the accuracy still needs to be improved. In this article, we propose a failure prediction method for hard disk drives based on a part-voting random forest, which differentiates prediction of failures in a coarse-grained manner. We conduct groups of validation experiments on two real-world datasets, which contain the SMART data of 64,193 drives. 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