一种硬盘故障预测的非监督对抗学习方法

Unsupervised adversarial learning method for hard disk failure prediction

为解决传统监督学习方法在不均衡数据集异常检测上易出现过拟合的问题,提出一种非监督对抗学习方法用于硬盘故障预测。该方法使用长短期记忆神经网络和全连接层设计了一种可进行二次编码的自编码器,只需使用正常样本进行训练,通过减小样本重构误差和潜在向量之间的距离,使模型学习正常样本的数据分布,从而提高了模型的泛化能力。模型中还引入生成式对抗网络增强非监督学习的效果。在多个数据集上的对比实验显示,该方法对异常样本的召回率和精确度都高于传统的监督学习和半监督学习分类器,泛化能力更强。所以,该非监督对抗学习方法在硬盘故障预测上是有效的。

In order to solve the problem of over-fitting of traditional supervised learning methods in anomaly detection of unbalanced datasets,an unsupervised adversarial learning method is proposed for hard disk failure prediction.This method uses the long short-term memory neural network and fully connected layer to design an Autoencoder that can be used for secondary coding.Only normal samples are used for training.By reducing the reconstruction error and the distance between potential vectors,the model can learn the data distribution of normal samples,thus improving the generalization ability of the model.The model also introduces the generative adversarial network to enhance the effect of unsupervised learning.Experiments on several datasets show that the recall rate and precision of the proposed method are higher than those of traditional supervised learning and semi-supervised learning classifiers,and that its generalization ability is stronger.Therefore,the unsupervised adversarial learning method is effective in hard disk failure prediction.