## 3 Application for Failure-Recall Data with DCLNN Method
### Dynamic Time Warping Pre-Processing Before Data Modeling  
In this part, failure time datasets of different escalators are distinguished by the abovementioned indicators to find those excellent data sequences with inherent advantages (high stationarity and low complexity) before data modeling. In addition, other data sequences are matched similarly with the selected excellent data using DTW. The above process can be seen as a pre-processing for more accurate data modeling. Table 1 summarizes the stationary indicators and complexity indicators of the original failure time series. According to the comprehensive judgment from stationarity and complexity, the non-stationarity and complexity of escalator #3 is the smallest. The CSI of Escalator #3 is almost fifty times smaller than those of the others. In order to achieve the purpose of accurate prediction, it is necessary to reduce the influence of the other data disturbances during data modeling. The over-sampling characteristics of DTW can increase the number of time series while weakening the disturbances. Thus, DTW is used to warp other failure time curves with the data of the third escalator as a reference. The specific warping path and warped time series can be found in Figure 10.  
The white lines in the left of Figure 10 are the warping path and the grayscale maps show the warping distance between the two time series. Figures on the right show the original time series and warped time series. It is easy to see that the new data after DTW have the same physical meaning as original data for the special warping path. Here, a specific analysis of the warped failure time series of Escalator #1 is given. As can be seen from Figure 10, whether it is the red reference sequence #3 or the blue warped sequence #1, no new failure time is generated on the \(y\)-axis, but only extended on the \(x\)-axis, where the \(x\)-axis represents the order or number of failures. Although some new samples were increased on the \(x\)-axis, the occurrence time of failure is the same as before. It can still be regarded as the same failure. This is unambiguous in objective meaning. Meanwhile, it can be seen that due to the oversampling characteristics of DTW, the length of the time series after being warped has been increased. Although the increased samples do not affect the physical meaning of the failure time series, it is possible to increase training samples and increase the network's understanding of the failure time series, which can further improve the training effect and prediction effect of the network. In order to show the function of DTW, above-mentioned stationary indicators and complexity indicators are calculated after DTW with escalator #3 as referenced. Table 2 shows the result after DTW pre-processing. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & **Indicator** & **E \#1** & **E \#2** & **E \#3** & **E \#4** & **E \#5** & **E \#6** \\ \hline \multirow{2}{*}{**SI**} & **std** & 144.5452 & 134.5956 & 80.0760 & 192.6437 & 136.8093 & 111.0329 \\  & \(p\)**-value** & 0.0245 & 0.1096 & 0.0046 & 0.0150 & 0.0672 & 0.0921 \\ \hline \multirow{2}{*}{**CI**} & **Lempel-ziv** & 0.4472 & 0.5209 & 0.3616 & 0.5000 & 0.5209 & 0.5209 \\  & **SampEn** & 0.5108 & 1.3863 & 0.2076 & 0.4418 & 1.0986 & 0.6931 \\ \hline \multirow{2}{*}{**CSI**} & **CSI** & 0.8090 & 10.6525 & 0.0277 & 0.6383 & 5.2611 & 3.6920 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comprehensive selection indicator before dynamic time warping (DTW).