# A Hybrid Degradation Modeling and Prognostic Method for the Multi-Modal System
## 3 Experiment and Discussion
### Experimental Simulation
#### 3.3.3 Discussion of Results
Finally, the model averaging is used to fuse the prediction results. After several experiments, the parameters of each sub-dataset are finally determined. Figure 10 illustrates the comparison of results for long-term cumulative degradation prediction, latest stage degradation prediction and fusion prediction for the same sample. Figure 10: The results of latest stage degradation prediction, long-term cumulative degradation prediction and hybrid prediction for number 148 in FD002. (**a**) The comparison between latest stage cumulative degradation prediction and actual values. (**b**) The comparison between long-term degradation prediction and actual value. (**c**) The comparison between hybrid prediction and actual values. Figure 9: The scores of different time window sizes for four sub-datasets. The experimental results show that the long-term cumulative prediction model is more accurate in predicting the remaining useful life of the system at the early stage of aging, and the latest stage degradation prediction model is more accurate at predicting the system in the late stage of aging. The hybrid prediction model combines the advantages of the two and has better overall prediction performance. No matter whether the system is in an early or late stage of the fault, it has a stabler prediction effect on the life cycle of the system. The estimated results based on the proposed model and the actual values of remaining useful life for the four sub-datasets' test samples are compared in Figure 11. In order to observe the performance of the prediction model intuitively, the test samples are arranged in descending order of the actual values. Figure 11 shows that the prediction results of the proposed model are close to the actual value of remaining useful life, and it is worth noting that the prediction ability of the model in the early stage of aging is weaker than that in the late stage of aging. The result is that the fault characteristics in the early stage of aging are not obvious, and with the aging of the system, the prediction accuracy is gradually enhanced. In addition, with the increase in the number of faults and the number of operational modes, it is more difficult to obtain an accurate prediction. However, the overall prediction performance of the model remains stable. When it is difficult to predict accurately, the prediction results tend to an early prediction as opposed to a late prediction. The proposed method is compared with other popular methods in this dataset to verify the superiority of the proposed method. The recurrent neural network (RNN), the long-short term memory network (LSTM), stacked autoencodesrs (SAEs), convolutional neural networks (CNN), the gradient boosting decision tree (GBDT) and support vector regression (SVR) as representative methods are evaluated in Table 3.  
Figure 11: The prediction results of the hybrid model for four sorted sub-datasets (FD001, FD002, FD003, FD004) are shown in (**a–d**) respectively. The tabular data shows that the model proposed in this paper has better evaluation metric values and has been proven to perform well in both overall prediction and late prediction. Comparing the predictions between four sub-datasets, it was found that to accurately predict the remaining useful life of the system working in multiple modes is more challenging than that of the system working in single mode.