基于BAS-BP模型的轴承剩余使用寿命预测

为有效评估轴承退化趋势，提高设备健康管理的智能化，提出一种基于BAS-BP模型的轴承剩余使用寿命预测方法。提取轴承全生命周期振动信号的时域和频域特征，构建18维退化特征；为提高神经网络的预测精度，采用天牛须搜索算法对初始权重和阈值进行优化，建立BAS-BP预测模型；通过在公开数据集上验证该模型的有效性。结果表明：所提模型可对轴承剩余寿命进行有效预测且精度较高。

Bearing Remaining Useful Life Prediction Based on BAS-BP Model

In order to effectively evaluate the bearing degradation trend and improve the intelligence of equipment health management, a bearing remaining useful life prediction method based on BAS-BP model was proposed. The time and frequency domain characteristics of bearing life cycle vibration signals were extracted, and the 18 dimensional degradation characteristics were constructed. In order to improve the prediction accuracy of neural network, the initial weight and threshold were optimized by beetle antennae search (BAS) algorithm, and the BAS-BP prediction model was established. The effectiveness of the model was verified on the public data set. The experimental results show that the BAS-BP model can effectively predict the remaining life of the bearing and has high accuracy.