# RUL Prediction of Rolling Bearings Based on a DCAE and CNN
###### Abstract  
Predicting the remaining useful life (RUL) of mechanical equipment can improve production efficiency while effectively reducing the life cycle cost and failure rate. This paper proposes a method for predicting the remaining service life of equipment through a combination of a deep convolutional autoencoder (DCAE) and a convolutional neural network (CNN). For rolling bearings, a health indicator (HI) could be built by combining DCAE and self-organizing map (SOM) networks, performing more advanced characterization against the original vibration data and modeling the degradation state of the rolling bearings. The HI serves as the label of the original vibration data, and the original data with such label is input into the prediction model of the RUL based on a one-dimensional convolutional neural network (ID-CNN). The model was trained for predicting the RUL of a rolling bearing. The bearing degradation dataset was evaluated to verify the method's effectiveness. The results demonstrate that the constructed HI can characterize the bearing degradation state effectively and that the method of predicting the RUL can accurately predict the bearing degradation trend. RUL; DCAE; CNN; rolling bearing; health indicator +
Footnote †: journal: Applied Sciences  
obtaining a highly precise physical model, experiments and calculations must be conducted substantially, resulting in high implementation costs. In addition, in consideration of certain difficulties in establishing a universal physical failure model, its reusability is limited. As a result, data-driven remaining useful life prediction methods have gradually begun to show advantages. Data-driven prediction methods are generally utilized to extract features that are sensitive to the current degradation state and to predict future damage by taking advantage of the existing historical data (observation data) upon obtaining a model that complies with the laws of nature. To predict the RUL of bearings, [PERSON] et al. proposed a method that combines the deep belief network with the particle filter [8]. [PERSON] et al. predicted the RUL of bearings based on the reliable degradation indicator and temporal convolution network with quantile regression [9]. [PERSON] et al. combined the convolutional autoencoder and continuous wavelet transform to construct a health indicator for bearings to predict the RUL [10]. [PERSON] et al. applied a stacked autoencoder (SAE) to the extraction of the vibration signal characteristics of a bearing and predicted the RUL based on the deep neural network [11]. [PERSON] et al. used a long- and short-term memory (LSTM) network to monitor how a machine ran and predicted the remaining service life of the machine components [12]. [PERSON] et al. applied the multi-time scale method to the prediction of the RUL of rolling bearings, established a multi-dimensional dynamic system based on phase space warping (PSW) technology and finally predicted the RUL of bearings successfully and quickly by combining the system with the improved Paris model [13]. Some researchers combined the physical model with the data-driven method based on deep learning and have achieved good results in RUL prediction for rolling bearings [14; 15; 16; 17; 18]. Through the current research results of scholars, it can be found that the main processes of the life analysis and prediction methods of mechanical equipment are data acquisition, degradation state modeling and RUL prediction. In the degradation state modeling stage, the main research content includes HI construction and health stage (HS) division. In the past, the construction of HI required more expert experience and more human intervention. At present, the magnitude of the data is getting larger, the signal state is becoming more and more complex, and the poor feature extraction ability of traditional methods and the insufficient nonlinear function mapping ability are gradually exposed.