# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
###### Abstract
[18] used stacked autoencoders and long- and short-term memory (LSTM) neural networks to create a model with standard deviation inputs and health indicator training labels for bearing life prediction. This method solves the problem of low prediction accuracy due to insufficient sample data, but the features to be fused are artificially selected. [PERSON] et al. [19] suggested a novel dual attention gated recurrent unit (GRU) neural network for bearing life estimation. This method has strong long-term and short-term prediction capability, but increases the complexity of the network structure. [PERSON] et al. [20] proposed a method that is used to predict the bearings' RUL on the basis of the improved temporal convolutional networks (TCN) and the migration learning, increasing the prediction precision of RUL. This method maintains high accuracy even when the training data are unlabeled, but it has the limitation of low prediction accuracy when the data distribution of source and target domains are different. [PERSON] et al. [21] proposed a multi-scale feature extraction approach combining expanded convolutional neural network (CNN) and LSTM network, and assigned weights to each degraded feature using a channel attention mechanism to forecast the bearings' RUL. [PERSON] et al. [22] proposed an approach for forecast machinery's RUL based on deep LSTM network. [PERSON] et al. [23] suggested a convolutional bi-directional long- and short-term memory (BiLSTM) network with attention mechanism to predict bearings' RUL. These deep learning methods do not rely excessively on the physical parameters and operating environment of the predicted object and have promising applications in practice. They solve the problem of inefficient and subjective manual extraction of features that exists in traditional machine learning. However, most networks use single-domain signals or integrated signals from multiple domains fed together into the model. Noteworthily, the time domain signals of bearings have time-varying vibration characteristics, and the frequency domain signals of bearings have frequency characteristics that do not vary with time, so a single time or frequency domain signal cannot accurately capture the detailed characteristics of bearing degradation, resulting in poor prediction accuracy. Therefore, bearing life prediction based on deep learning methods considering multi-domain features has become a research direction. [PERSON] et al. [24] used reduced-dimensional time-domain features and time-frequency-domain features fed into a parallel deep residual network designed for the purpose of predicting the bearings' RUL prediction, thus improving the bearing life prediction accuracy. This network structure has a strong feature extraction capability, but it is lacking for feature extraction of bearing data with time-series. According to the reasons above, this paper proposed a method on the basis of multi-branch improved CNN network (MBCNN) and BiLSTM network (MBCNN-BiLSTM) with a global attention mechanism to predict bearings' RUL. The MBCNN network, which uses simultaneous processing of the original signal and its fast Fourier transformed frequency domain signal, is designed to fully exploit its spatial feature information. Then, the acquired features are input to the designed BiLSTM network to further acquire the temporal feature information. A global attention mechanism is introduced to better focus on the temporal and spatial key features to enhance the useful information of bearing degradation while suppressing the interference of useless information. The validity and feasibility for the proposed method were verified using a publicly available bearing dataset. The main highlights of this paper are as follows:  
* The proposed bearing RUL prediction method is an end-to-end prediction method that does not rely on manual experience and sets the degradation threshold. The original data are input into the designed model, and the results of the bearing's RUL are obtained directly.