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A Two-Step Parametric Method for Failure Prediction in Hard Disk Drives

机译：硬盘驱动器故障预测的两步参数化方法

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Predicting the impending failure of hard disk drives (HDDs) is crucial for preventing essential data from losing. In this paper, a two-step parametric method was developed to predict the impending failure of HDDs using the aggregate of statistical models. This method deals with the problem of failure prediction in two steps: anomaly detection and failure prediction. First, Mahalanobis distance was used for aggregating all the monitored variables into one index, which was then transformed into Gaussian variables by Box–Cox transformation. By defining an appropriate threshold, anomalies in HDDs were detected as a result. Second, a sliding-window-based generalized likelihood ratio test was proposed to track the anomaly progression in an HDD. When the occurrence of anomalies in a time interval is found to be statistically significant, indicating the HDD is approaching failure. In this work, we also derived a new cost function to adjust the prediction rate. This is important in a way to balance the failure detection rate and false alarm rate as well as to provide an advanced warning of HDD failures to the users, whereby the users can back up their data in time. Then the developed method was applied on a synthetic data set showing its effectiveness on predicting failures. To demonstrate the practical usefulness, this method was also applied on a real-life HDD data set. The result shows that our method could achieve 68% failure detection rate with 0% false alarm rate. This is much better than the results achieved by the state-of-the-art methods, such as support vector machine and hidden Markov models.
机译：预测硬盘驱动器（HDD）即将发生的故障对于防止基本数据丢失至关重要。在本文中，开发了一种两步参数方法来使用统计模型的集合来预测HDD的即将发生的故障。该方法分两个步骤处理故障预测问题：异常检测和故障预测。首先，使用马氏距离将所有监控变量汇总为一个索引，然后通过Box-Cox变换将其转换为高斯变量。通过定义适当的阈值，可以检测到HDD中的异常。其次，提出了一种基于滑动窗口的广义似然比检验来跟踪HDD中的异常进程。如果发现某个时间间隔内异常的发生在统计上很重要，则表明HDD即将发生故障。在这项工作中，我们还导出了新的成本函数来调整预测率。这对于平衡故障检测率和误报率以及向用户提供HDD故障的高级警告非常重要，从而使用户可以及时备份其数据。然后将开发的方法应用于合成数据集，显示其在预测故障方面的有效性。为了证明实际的实用性，此方法还应用于实际的HDD数据集。结果表明，该方法可以实现68％的故障检测率，误报率为0％。这比通过最新方法（例如支持向量机和隐马尔可夫模型）获得的结果要好得多。

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