# Hard Disk Failure Prediction Based on Blending Ensemble Learning
## 4 Experiment
### Experiments and Results
In most experimental groups, LSTM model experimental results are better than using BP neural networks alone but not better than blending ensemble learning methods. In addition, from the results in Table 4, in most cases, it can be seen that LSTM, when added to the base learner, can significantly improve the performance. The line chart in Figure 11 more clearly shows the effect changes after the addition of the LSTM to base learners, which proved the effectiveness of this method. However, it should be noted that there are cases in which the LSTM model has the best effect in some individual experimental groups. We guess that under such dataset processing, if the ratio of the training set to the test set is too large or too small, the performance of the LSTM model will be poor or the results will not be accurate. Because the total amount of datasets is fixed, when the proportion of training sets is too large, the number of test sets is too small, which will lead to the insufficient persuasiveness of test results. In contrast, when the proportion of test sets is large, too few training sets will result in insufficient model training. When the ratio is between 1:1 and 1:0.5, the performance of the LSTM network can be maximized, and when the ratio is around this, the effect of the model is more in line with expectations. However, this conclusion is only a guess and needs to be verified by subsequent experiments. Figure 11: _Cont._  
Figure 10: Comparison histogram of experimental results of models under different ratios of training set to test set. In addition, compared with the direct use of the BP neural network method, the blending ensemble learning method will also greatly reduce the running time and improve the efficiency of training and testing. Table 5 shows the training time of each model in some experimental groups. The BP neural networks used are all 10,000 epochs. The reason for this gap in training time is that the size of the new training set used in the vlending ensemble learning method is reduced. Because it is a part of the original training set, the size of the training set is actually the size of the split validation set, and the complexity of the new training set and test set is also reduced. Finally, to test the universality of our method, we counted data for six different models of hard disks. In addition to using Seagate hard disks from the same manufacturer as the training data, we counted hard disks from two different hard disk manufacturers, Hitachi and Toshiba. The statistics for the six groups of hard disks used for testing are shown in Table 6. These six different models of hard disks cover three manufacturers and have capacities of 4 TB, 8 TB, 12 TB and 16 TB, and the number of failed hard disks counted ranges from 79 to nearly 1000. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Manufacturer** & **Capacity** & **Number of Failed Hard Disks** \\ \hline ST8000 DM002 & Seagate & 8 TB & 613 \\ ST8000 NM0055 & Seagate & 8 TB & 988 \\ ST12000 NM0008 & Seagate & 12 TB & 661 \\ HMS5C4040 BLE640 & Hitachi & 4 TB & 305 \\ MG07 ACA14 TA & Toshiba & 12 TB & 625 \\ MG08 ACA16 TE & Toshiba & 16 TB & 79 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistics of other models of hard disks used for testing. Figure 11: (a) Comparison of experimental results between blending group A and blending group C. (b) Comparison of experimental results between blending group B and blending group D. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ratio** & **Blending Group A** & **Blending Group C** & **Blending Group B** & **Blending Group D** & **BPNN** \\ \hline
1:13.25 & 21s753 ms & 23s248 ms & **20s600 ms** & 21s857 ms & 135s844 ms \\
1:1.06 & 182s267 ms & **172s410 ms** & 188s113 ms & 178s105 ms & 942s349 ms \\
1:0.075 & 350s698 ms & 366s672 ms & 355s793 ms & **317s722 ms** & 1664s662 ms \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Partial comparison of model training time (The data in bold is the shortest running time under the same conditions).