## 5 Results and Discussions  
In order to further explain the influence of the degradation model which cannot be accurately obtained in-service stage on the prediction results, we randomly selected 30 sets of life cycle data from Set #2 and Set #4 respectively, then the first 1/2, 2/3, 3/4, and 4/5 of the selected life cycle data were used as the test samples. Since there was not one similar life candidate set selected in the process of life prediction by STM, the degradation model was calculated only according to the mean and variance which were from each operating condition of service equipment in-service stage. Finally, STM was used to predict the life based on the degradation model and original monitoring data respectively, and the MAE, RMSE, MAPE, and MASE were calculated as shown in Figures 6 and 7. As shown in Figures 6 and 7, in the same scenario that the life was predicted by STM, the evaluation indicators of prediction results based on the degradation model were significantly larger than those based on original monitoring data. Moreover, the evaluation indicators of prediction results based on the degradation model were not regular under the different lengths of test samples, while those based on original monitoring data shown a downward trend with the increase of test sample length. Therefore, in the scenario of multiple operating conditions, the degradation model obtained only based on part of the monitoring data of the service equipment cannot be completely equivalent to that obtained by whole life, and the accurate life prediction results cannot be obtained in the process of life prediction. Figure 5: Degradation model comparison based on different mean and variance. (**a**) Life cycle monitoring Data 1 of Sensor Channel 3. (**b**) The first half monitoring Data 1 of Sensor Channel 3. (**c**) Life cycle monitoring Data 2 of Sensor Channel 3. (**d**) Degradation model based on the mean and variance of life cycle monitoring data. (**e**) Degradation model comparison based on the mean and variance of the first half of monitoring Data 1. (**f**) degradation model comparison based on the mean and variance of life cycle monitoring data 2. Figure 6: MAE, RMSE, MAPE, and MASE results of Set #2 based on the different test dataset. (a) MAE. (b) RMSE. (c) MAPE. (d) MASE. Figure 7: MAE, RMSE, MAPE, and MASE results of Set #4 based on the different test dataset. (a) MAE. (b) RMSE. (c) MAPE. (d) MASE. Consequently, the proposed method improved the life prediction results under multiple operating conditions by improving the accuracy of direct measurement of monitoring data similarity. Likewise, 30 sets of life cycle data from Set #2 and Set #4 were selected randomly respectively, then the first 1/2, 2/3, 3/4, and 4/5 of the selected life cycle data were used as the test samples. Finally, MP-SAX-STM was used for life prediction and compared with the prediction results of STM which was based on original monitoring data, and the MAE, RMSE, MAPE, and MASE were calculated as shown in Figures 8 and 9. Where the subjective weight determination method was selected when calculating the similarity by MP-SAX, in which the weight of trend component is 0.75 and the weight of detail component is 0.25. As shown in Figures 8 and 9, when only monitoring data were used for life prediction under multiple operating conditions, the evaluation indicators of MP-SAX-STM were lower than those of STM. Furthermore, with the increase of the length of the test sample, the evaluation indicators of MP-SAX-STM and STM showed a downward trend. Furthermore, for Set #2 and Set #4, the relative reduction of each indicator of MP-SAX-STM compared with STM was shown in Tables 3 and 4, respectively. As shown in Tables 3 and 4, there was little difference among the relative change of each indicator under the same length. For Set #2, there was little difference in the change of the same indicator under different lengths, while, for Set #4, the variation under same indicator and different lengths increased approximately with the increase of length.