## 4 Conclusions  
Failure time, as an important factor for the safe operation of escalators, is used for prediction by the proposed DCLNN neural network in this paper. Due to the short length of the failure time series, and the characteristics of random interference and non-uniform sampling, the failure time prediction is much more difficult. Naturally, higher data quality and a suitable neural network can help solve this problem. Considering the oversampling characteristics and similarity-matching performance of DTW, some inferior data with low stationarity and high complexity can be warped by referring to excellent data while retaining its original physical meaning. Such DTW-based data pre-processing can not only increase the sample numbers of short time series but also reduce the impact of disturbance data on the neural network. After data preprocessing, the combination of CNN and LSTM is used to train and predict the failure time series. With the help of CNN's feature extraction ability, high-dimensional features can be trained to understand the failure time series more comprehensively. Then, these high-dimensional features can be used for time series prediction in LSTM. Based on the advantages of DTW, CNN, and LSTM, a strategy to combine data quality enhancement with deep neural networks is proposed to achieve the goal of failure time prediction for an escalator. Failure-recall data of an escalator are analyzed using the proposed DCLNN method, and the result shows that the proposed method can effectively reduce the root-mean-square error between the predicted value and the real value in the prediction process. At the same time, compared to some classical time series prediction methods such as the Kalman Filter, Nonlinear Auto Regressive Neural Network, and Elman RNN, the prediction accuracy of the proposed method is obviously improved. Besides, the function of the DTW was further analyzed to show that enhancing data quality can effectively improve the accuracy of prediction. The proposed DCLNN method can reduce the prediction time error of escalator failure to less than one month, which can provide scientific guidance for smarter maintenance planning and economic improvement.  
However, due to the limited number of samples, the accuracy of the proposed method needs to be improved for better maintenance guiding, and a more reasonable threshold of CSI indicators needs further study before pre-processing in engineering application. Furthermore, the proposed method is currently applicable to the situation of two or more devices to improve the prediction accuracy. In the future, an in-depth study is necessary for the failure time series prediction problem of a single device under a small number of samples.  
[PERSON] proposed the idea of DCLNN, analyzed the failure data, and wrote the paper; [PERSON] proofread the manuscript; [PERSON] contributed to the conclusion and guided the manuscript; [PERSON] and [PERSON] collected the failure data. All authors have read and agreed to the published version of the manuscript.  
Figure 18: Statistics of RMSE in prediction process. (**a**) RMSE of every escalator after DTW. (**b**) RMSE variation statistics after DTW.  
**Funding:** This work was supported in part by the National Key Research and Development Program of China under Grant 2017 YFC0805701 and National Natural Science Foundation of China (No. 51775411).  
**Acknowledgments:** The authors would like to sincerely thank all the anonymous reviewers for the valuable comments that greatly helped to improve the manuscript.  
**Conflicts of Interest:** The authors declare no conflict of interest.