# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
###### Abstract
As a result, there is an urgent need to find new intelligent methods for more accurate and efficient degradation state modeling of mechanical equipment. The prediction method of the bearing RUL based on deep learning has gradually become the mainstream [19; 20; 21; 22; 23; 24]. The research results of many scholars show that the feature information of bearings can be adaptively extracted from the original data by using the deep learning method, and the deep learning method has a strong capability of nonlinear function relation mapping [25; 26; 27; 28; 29]. Scholars have achieved good results in processing massive data by using the features of local weight sharing and the sparse connection of a CNN, and the improved algorithm based on a recurrent neural network (RNN) can process time series data well. At present, most of these prediction methods belong to supervised learning methods. One of the necessary requirements for training a good supervised learning method model is that data labeling information is sufficient and accurate; that is, data can be accurately labeled. At present, most research methods add labels to data by linear or segmented marking, which is inaccurate in the description of data information. Therefore, it needs to be improved in the data processing stage. As the high-precision mechanical equipment develops and the monitoring equipment rapidly upgrades, higher requirements have been placed on the prediction of the RUL of rolling bearings in the context of massive monitoring data, such as how to extract useful information from massive data and how to accurately model the degradation state of rolling bearings [30; 31]. The purpose of this paper is to propose an intelligent feature self-extracting bearing degradation state modeling method independent of expert experience, propose a more accurate life prediction model and establish degradation state modeling (i.e., health factors) and a related RUL method. Therefore, a rolling bearing RUL prediction method based on a DCAE and a CNN is proposed. The overall life prediction process is divided into two parts as shown in Figure 1. The first part is the modeling of rolling bearing degradation based on one-dimensional vibration monitoring data. In the stage of modeling the bearing degradation state, the improved one-dimensional convolution autoencoder is mainly selected to extract the characteristics of the vibration data. The CAE has good denoising ability. The training of the CAE adopts an unsupervised method and does not rely on expert experience or complex signal-processing technology. The CNN has the characteristics of weight sharing and sparse connections. This feature is very suitable for processing large data volumes and full life cycle data. A one-dimensional convolution kernel is selected in the convolution operation, and the one-dimensional convolution kernel is more suitable for processing vibration data based on a time series. The original data are characterized by a one-dimensional convolutional autoencoder, and then the HI value of the bearing is obtained by the SOM feature reduction technique. In the RUL prediction stage, a 1D-CNN model is proposed to predict the RUL of the rolling bearing. On the basis of obtaining the HI in the degradation state modeling stage, the original vibration signal is processed in segments, and the constructed HI is used as the label of the segmented data and input into the 1D-CNN model for RUL prediction. This paper is organized as follows. Section 2 briefly introduces the basic theory of the rolling bearing degradation state and the required methods. Section 3 describes the DCAE-based bearing degradation state modeling scheme (i.e., HI acquisition). The content of Section 4 is the proposal of the RUL prediction method based on 1D-CNN, the experimental results and algorithm evaluation. The conclusions of the full paper are given in Section 5.