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.

Remaining Useful Life Estimation of Turbofan Engines with Deep Learning Using Change-Point Detection Based Labeling and Feature Engineering

“…In [44], an LSTM and gradient boosting machine (GBM) were utilized to analyze Li-ion batteries, combined with explainable artificial intelligence techniques for feature selection. The study in [45] introduces a novel approach for improving the accuracy of bearings' RUL prediction, combining a multi-branch convolutional network (MBCNN) with global attention and a BiLSTM network, utilizing both spatial and timing features from vibration signals, which were ultimately tested on a public bearing degradation dataset. Also, the significance of RUL labeling on RUL prediction is demonstrated in the study by [46] with load calculations, which assessed bearings.…”