Forecasting the failure of hard disk drives is important in server operation and has attracted increasing attention. However, current disk drive warning systems suffer from high false positive rates and high resource consumption when dealing with hard disk drive overall failure. Therefore, to accurately and stably predict hard disk drive overall failure, this paper develops a new Weibull Log-Logistic grey forecasting model with multiple interaction effects. Firstly, to capture and fit the trend of hard disk failure data flexibly and reduce the volatility, a new accumulation generation operator is established by introducing the Weibull Log-Logistic mixture distribution. Secondly, the proposed model constructs a multiple interaction term to describe the nonlinear relationship of the dependent variables on the system behavior series and the multiple interaction effects between the independent variables. Then, the parameter estimation method is designed to improve the fitting accuracy of the new model, in which linear estimation method, nonlinear estimation method, and meta-heuristic optimization algorithm are used to calculate the parameter values according to their categories. Finally, four varieties of hard disk drive data sets from BackBlaze are selected as study cases to validate the effectiveness of the proposed model. The results show that the mean MAPE of the proposed method is 2.1181 %, 2.2306 %, 8.1712 %, and 4.3417 %, respectively, corresponding to (hard disk drives ST8000NM0055, ST12000NM0007, ST10000NM0086, and ST14000NM0138), and the average value of the evaluation metrics are optimal in all competing models. Furthermore, it is observed that the number of reallocated sectors has the greatest influence in causing the failure of hard disk drives.

中文翻译：

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硬盘驱动器故障的新 Weibull Log-Logistic 灰色预测模型

预测硬盘驱动器的故障对于服务器运行非常重要，并引起了越来越多的关注。然而，当前的磁盘驱动器警告系统在处理硬盘驱动器整体故障时存在高误报率和高资源消耗的问题。因此，为了准确、稳定地预测硬盘驱动器整体故障，本文开发了一种新的具有多重交互作用的Weibull Log-Logistic灰色预测模型。首先，为了灵活捕捉和拟合硬盘故障数据的趋势，降低波动性，引入Weibull Log-Logistic混合分布，建立了一种新的累加生成算子。其次，该模型构造了多重交互作用项来描述因变量对系统行为序列的非线性关系以及自变量之间的多重交互作用。然后，设计了参数估计方法，以提高新模型的拟合精度，其中使用线性估计方法、非线性估计方法和元启发式优化算法根据类别计算参数值。最后，选择 BackBlaze 的四种硬盘驱动器数据集作为研究案例来验证所提模型的有效性。结果表明，该方法的平均MAPE分别为2.1181％、2.2306％、8.1712％和4.3417％，分别对应于（硬盘驱动器ST8000NM0055、ST12000NM0007、ST10000NM0086和ST14000NM0138），以及评估的平均值指标在所有竞争模型中都是最佳的。此外，据观察，重新分配的扇区数量对导致硬盘驱动器故障的影响最大。