The Next Failure Time Prediction of Escalators via Deep Neural Network with Dynamic Time Warping Preprocessing

Applied Sciences

Jingsong Xie¹  Zitong Zhou²  Yanyang Zi²  Tong An²  Jinglong Chen² 

[1] School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China;State Key Laboratory for Manufacturing and Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China;

关键词: failure time prediction;    convolutional neural network;    long-short term memory;    dynamic time warping;    escalator;   

DOI  :  10.3390/app10165622

来源: DOAJ

【 摘 要 】

The escalator is one of the most popular travel methods in public places, and the safe working of the escalator is significant. Accurately predicting the escalator failure time can provide scientific guidance for maintenance to avoid accidents. However, failure data have features of short length, non-uniform sampling, and random interference, which makes the data modeling difficult. Therefore, a strategy that combines data quality enhancement with deep neural networks is proposed for escalator failure time prediction in this paper. First, a comprehensive selection indicator (CSI) that can describe the stationarity and complexity of time series is established to select inherently excellent failure sequences. According to the CSI, failure sequences with high stationarity and low complexity are selected as the referenced sequences to enhance the quality of other failure sequences by using dynamic time warping preprocessing. Secondly, a deep neural network combining the advantages of a convolutional neural network and long short-term memory is built to train and predict quality-enhanced failure sequences. Finally, the failure-recall record of six escalators used for 6 years is analyzed by using the proposed method as a case study, and the results show that the proposed method can reduce the average prediction error of failure time to less than one month.

【 授权许可】

Unknown