Thus, it is necessary to predict the failure time accurately and timely, and arrange maintenance activities in advance, which can effectively reduce the failure rate and reduce accidents. Failure time series modeling analysis can describe the law of failure occurrence by establishing a corresponding mathematical model of related failure data. Many scholars have done in-depth research on time series analysis in safety research, such as failure maintenance time based on the Weibull distribution [7], shipyard occupational risk assessment based on multivariate regression and genetic algorithm [8], and the Akaike Information Criterion (AIC) based steel mill alarm mechanism [9]. These methods have achieved good effects in the case of large amounts of data. However, for accident-related failure data, it has special features of short samples, non-uniform sampling, and random interference. Traditional data modeling based on statistical laws or deterministic mathematical models requires more labelled data, while data related to safety accidents have typical characteristics as aforementioned, making data modeling problems much more difficult. Neural network technology, as one of the most popular data analysis methods, can build a model containing complex nonlinear relationships without deliberate attention to the mathematical characteristics of the data itself, and has good generalization ability to map the relationship between input and output quickly and effectively, which has been widely used in many fields. In recent years, with the development of neural network (NN) technology, neural networks have been used in various fields [10; 11; 12]. The data modeling by NNs does not deliberately focus on the mathematical characteristics of the data itself but learns to construct the complex relationship between input and output with neurons. Thus, neural networks have good generalization capacity. However, the accuracy of neural networks is sensitive to disturbance in data. In addition, some neural networks only pay attention to the relationship between input and output, ignoring the hidden information of the input themselves, which is unreasonable for time series modeling and prediction. Long short-term memory (LSTM), as one of the recurrent neural networks (RNNs), was proposed to solve the problem of time series prediction [13]. LSTM can analyze and process time series with unknown duration delay. LSTM can improve the memory module of traditional RNNs and avoid the problem where effective historical information cannot be stored for a long time, because of the constant input data [14]. LSTM has also been utilized in time series prediction [15], remaining useful for life prediction [16] and safety analysis [17; 18]. However, LSTM does not learn well the characteristics of the data itself for short time series with too few samples. Therefore, this paper considers the short-sequence feature extraction ability of a convolutional neural network (CNN) to extract high-dimensional features, which can help the LSTM unit to understand the data better. In addition, the combination of CNNs and LSTMs in a unified framework has already offered state-of-the-art results in the speech recognition domain [19], health care [20], and power load prediction [21]. In addition, the quality of the data itself makes a big difference to the accuracy of neural network modeling. Meanwhile, considering the short length and random interference in the failure data, it is meaningful to enhance the data quality to improve the prediction accuracy of the neural network before data modeling. Dynamic time warping (DTW) [22], with characteristics of oversampling and similarity matching, is much more suitable for data preprocessing before data modeling, especially for short and interfering failure data. DTW has also been used in many fields [23; 24].