# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 1 Introduction
### Related Work  
[PERSON] et al. [3] improved the distribution-free statistical hypothesis testing algorithm that replaced the maximum error threshold warning algorithm and proposed two new methods based on this mathematical statistical approach, namely the ORing single-attribute rank-sum test and the multivariate rank-sum test. These authors also conducted experiments based on S.M.A.R.T. characteristics to obtain fault warning accuracy rates of about 40-60% and a false alarm rate of around 0.2-0.5%. [PERSON] et al. [4] proposed a dynamic tracking method for the prediction of hard disk failure based on a switchable state stochastic process model. The Rao-Blackwelled particle filter was used to update model estimates and parameters, and a dynamic failure threshold was designed. However, the overall framework was complex, and the universality was not strong. Compared to mathematical statistical methods, the machine learning technology that has been rising and developing in recent years just meets the needs of people, and various methods can achieve good results. [PERSON] et al. [5] counted four feature selection methods and eight machine learning methods for anomaly detection. Through permutations and combinations, they conducted experiments on two datasets and obtained the combination of feature selection methods and anomaly detection methods that had the best effect at that time, providing a good idea and direction for subsequent researchers. [PERSON] et al. experimented with the naive Bayes, decision tree, and random forest algorithms and used accuracy, precision, AUC, and running time as criteria that indicated that the random forest algorithm had the best performance. Furthermore, with the proposal and development of deep learning, neural networks have come into people's view. Various neural networks that can perform complex operations and process time series data can predict hard disk failures more accurately and efficiently. [PERSON] et al. [1] used an LSTM network to process time series data and obtained better results than machine learning methods. However, the model may age with long-term use, and the model structure needs to be further improved. Based on the work done by [PERSON], [PERSON] et al. [6] improved the previous network structure and focused their work on solving the problem of data imbalance. They used a partial undersampling method to obtain better results but with insufficient optimization of the model and without considering the use of better sampling methods. After the spread of bidirectional LSTM networks, [PERSON] [2] proposed a new method of data standardization. The new model was used for experiments. Different from the previous methods that performed classification tasks, it was used to predict the remaining life of hard disks, and the curve fitting was very good. [PERSON] et al. [7] introduced a new convolution network known as TCN (temporal convolutional network) for time series analysis and used SMOTE (synthetic minority over-sampling technique) to deal with the problem of data imbalance. [PERSON] et al. [8] not only used the characteristics of the hard disk itself but also considered the influence of the neighborhood of the hard disk and proposed a new sampling method, TPS (temporal progressive sampling), to solve the influence of data imbalance. [PERSON] [9] took S.M.A.R.T. as the learning feature and added the performance data of hard disks and servers as well as the influence of the spatial location of the hard disk in reality. These authors used a new neural network known as CNN-LSTM for the experiments. The final results showed that the experimental results considering all three indicators are the best. Table 1 shows the methods used by previous researchers and the results obtained. According to the models and methods used by predecessors, we notice that all the above methods used for hard disk failure prediction are simple machine learning or deep learning methods, which generally have the disadvantages of long running times and require a large amount of training data.