Large scale data storage is susceptible to failure. As disks are damaged and replaced, traditional machine learning models, which rely on historical data to make predictions, struggle to accurately predict disk failures. This paper presents a novel method for predicting disk failures by leveraging multi-layer domain adaptive learning techniques. First, disk data with numerous faults is selected as the source domain, and disk data with fewer faults is selected as the target domain. A training of the feature extraction network is performed with the selected origin and destination domains. The contrast between the two domains facilitates the transfer of diagnostic knowledge from the domain of source and target. According to the experimental findings, it has been demonstrated that the proposed technique can generate a reliable prediction model and improve the ability to predict failures on disk data with few failure samples.

本文提出了一种新的方法来预测硬盘故障，通过利用多层领域自适应学习技术，在选择了具有众多故障的硬盘数据作为源域和少量故障的数据作为目标域的前提下，进行特征提取网络的训练，从而实现从源域到目标域的诊断知识转移。实验结果表明，该方法可以生成可靠的预测模型，并提高对少量故障样本硬盘数据的故障预测能力。

基于多层域自适应学习的磁盘故障预测