Hard Disk Drive Failure Prediction Based on GRU Neural Network

Abstract

Hard disk drive (HDD) failure prediction is critical for data center maintenance. The conventional long short-term memory (LSTM) neural networks have been successfully used to predict the failure of HDD. However, the short degradation cycle of the disk leads to a severe imbalance in the ratio of healthy data to failure data, which degrades the performance of LSTM neural networks. Moreover, the complexity of LSTM neural networks is also very high. This paper proposes a disk failure prediction approach based on an intelligent attribute gated recurrent unit (GRU) neural network and TimeGAN adversarial network, the GRU neural network can adapt to the impact of long hard disk data sequences, while the TimeGAN can address the data imbalance problem. Experiments performed on Backblaze data demonstrate that our proposed approach achieves better performances in terms of failure detection rate than then conventional LSTM.