| Citation: | LIU Xiyang, CHEN Guo, HAO Tengfei, et al. Bearing fault size estimation based on convolutional bidirectional long and short term memory networks[J]. Journal of Aerospace Power, 2023, 38（4）：1005-1016 doi: 10.13224/j.cnki.jasp.20210292
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The damage size identification of aero-engine rolling bearing based on vibration monitoring data is of great significance to the study of rolling bearing fault evolution, prediction and diagnosis. In view of inherent restrictions in traditional identification models such as high dependence on prior knowledge, insufficient feature extraction and limited category of training fault sizes, a prediction method of rolling bearing damage size based on deep learning was proposed, which can accurately identify the middle sizes that did not appear in the training process. A combined model of deep convolutional long-short-term memory network was developed, which can sufficiently extract the multi-dimensional and time-series characteristics of bearing vibration signal, and realize the intelligent and efficient diagnosis of bearing fault. On the basis of theoretical analysis, the rolling bearing fault tests under various damage sizes and rotational velocities were carried out by using the accelerated fatigue testing machine for rolling bearings, and the traditional and novel methods were compared based on the test data. The results showed that the prediction accuracy of the combined network can reach 99.94% and 98.67%, respectively, under normal and noisy conditions, higher than the single deep convolution network, long-short-term memory network and other models. The comparison results amply demonstrate the superiority of the proposed method.
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