# Research on Remaining Useful Life Prediction of Bearings Based on MBCNN-BiLSTM
###### Abstract  
For safe maintenance and to reduce the risk of mechanical faults, the remaining useful life (RUL) estimate of bearings is significant. The typical methods of bearings' RUL prediction suffer from low prediction accuracy because of the difficulty in extracting features. With the aim of improving the accuracy of RUL prediction, an approach based on multi-branch improved convolutional network (MBCNN) with global attention mechanism combined with bi-directional long- and short-term memory (BiLSTM) network is proposed for bearings' RUL prediction. Firstly, the original vibration signal is fast Fourier transformed to obtain the frequency domain signal and then normalized. Secondly, the original signal and the frequency domain signal are input into the designed MBCNN network as two branches to extract the spatial features, and then input into the BiLSTM network to further extract the timing features, and the RUL of bearings is mapped by the fully connected network to achieve the purpose of prediction. Finally, an example validation was performed on a publicly available bearing degradation dataset. Compared with some existing prediction methods, the mean absolute and root mean square errors of the predictions were reduced by \"22.2%\" to \"50.0%\" and \"26.1%\" to \"52.8%\", respectively, which proved the effectiveness and feasibility of the proposed method. bearing: remaining useful life prediction; deep learning; multi-branch neural networks; bi-directional long- and short-term memory +
Footnote †: journal: Medical  
of the prediction, but it needs to consider that the effects of stress, installation location and application increase the complexity of model building. [PERSON] et al. [8] proposed a linear and quadratic-function-based Kalman filter model for RUL prediction of bearings. It verifies the effectiveness of the proposed method with bearing operation-to-failure data, but it requires a reasonable selection of health indicators and predetermined thresholds. To forecast the bearings' RUL, [PERSON] et al. [9] introduced the Switching Unscented Kalman Filter (SUKF) algorithm. It still has high accuracy in continuously predicting the RUL, but there are some limitations in the selection of some fixed values. The literature in [10; 11; 12] builds the model by an adaptive approach and thus identifies defects in the object under test. Nevertheless, the above model-based methods have achieved good results in predicting the RUL of bearings. However, as the complexity of mechanical systems increases, it becomes increasingly difficult to construct physical model underneath one-of-a-kind operating conditions, hindering the growth of physical model-based techniques [13]. With the introduction of big data technology over recent years, RUL prediction primarily using data-driven techniques has emerged as an active research topic. Data-driven approaches in most cases apply methods such as machine learning or deep learning that use measurement data to reveal the dynamic properties of mechanical systems [14]. [PERSON] et al. [15] introduced a data-driven method to predict the bearings' RUL on the basis of \(\varepsilon\)-support vector regression. It is suitable for any kind of RUL prediction task, but there are more variables to control. [PERSON] et al. [16] proposed a two-stage prediction method based on an extreme learning machine for the RUL prediction of rolling bearings. It can maintain high prediction accuracy with limited training samples but it still requires artificial selection of health degradation indicators. [PERSON] et al. [17] proposed a hybrid model on the basis of PSO-SVM for the RUL prediction of aircraft engines and the method has good generalizability. In these traditional machine learning methods, we should manually extract the bearing degradation features, and there are problems such as low efficiency of manual feature extraction and strong subjectivity. As deep learning continues to evolve, deep neural networks are applied in the area of lifespan prediction over time. For example, [PERSON] et al.