###### Abstract
Based on the idea of deep learning, as an unsupervised learning method, autoencoder, is widely used in dimensionality reduction and information retrieval tasks in the field of PHM because of its robust feature extraction and generalization ability [9, 10, 11]. For the past few years, some experts and scholars have proposed to use autoencoder, or improved version of autoencoder, such as stacked sparse autoencoder [12], stacked denoising autoencoder [13,14], convolutional autoencoder [15], and to reconstruct sensor readings and automatically extract degradation characteristics to obtain unsupervised degradation indicator values to identify the severity of equipment degradation. The long short-term memory network (LSTM) [16], convolutional neural network (CNN) [17], deep convolution neural network (DCNN) [18], Convolution and LSTM Hybrid Deep Neural Networks [19], recursive gating unit (Gru) neural network [20], and recurrent convolutional neural network [21] are combined to realize the task of PHM. The above research work provides a useful reference for the effective learning of the degradation state characteristics of bearing operation. In particular, literature [22] realized the effective prediction of bearings RUL based on deep autoencoder and deep neural networks. On this basis, we will focus on the dimension, purity and efficiency of data. In practical engineering applications, the actual operation data and forecast model parameters of the bearing have a substantial uncertainty, so the traditional point prediction results will inevitably have errors, which is difficult to reflect the uncertainty of bearings degradation state. In the establishment of maintenance strategy based on reliability, if able to quantify the uncertainty of prediction results, they can run for maintenance decision-making and risk assessment to provide more abundant information. The probability prediction offers an effective way to quantitatively balance the risk, which can better describe the possible fluctuation range, uncertainty, and risk of the RUL in the process of future operation degradation, so it has more research value. For the probability density prediction, most studies adopt nonparametric modelings, such as quantile regression (QR) [23], kernel density estimation(KDE) [24], etc. Which can directly calculate the distribution function or quantile. [PERSON] [25] introduced a more flexible model in 2000, namely, the quantile regression neural network (QRNN); how ever, the shallow structure of QRNN lacks enough ability to simulate the complex time characteristics of time series model. With the continuous development of deep learning, some deep learning models have been gradually applied to the research of time series data. On this basis, considering the correlation between RUL and time, the previous time information can be associated with the current task, the LSTM network model [26] that can learn long-term dependence relationship is constructed. Compared with LSTM and other networks, CNN has a natural advantage in large-scale parallel data processing. Temporal convolution network (TCN) [27] is an improvement of one-dimensional CNN for time series problems, literature [27] shows that TCN has the advantages of faster prediction speed and higher accuracy in most scenarios. At present, there is little research on TCN in the field of engineering reliability, and there is no report on the research of fusion of TCN and neural network in the probability density prediction of RUL. This study mainly focuses on the problems such as the current running state of the motor rolling element bearings and RUL, which cannot truly represent the abnormal monitoring data caused by various reasons during the equipment condition monitoring process. In this paper, a degradation indicator (DI) construction method based on data reduction and key features mining analysis is proposed. The QR was combined with TCN to improve the performance of the prediction model.