基于跨域均值逼近的轴承剩余使用寿命预测

A Prediction Method of Bearing Remaining Useful Life Based on Cross Domain Mean Approximation

由于轴承退化数据较少及不同工况之间轴承数据分布差异较大,实现在一个轴承上训练的剩余寿命预测模型,能够预测其他同一工况或不同工况不同轴承的剩余使用寿命,是一个待解决的难题。本文提出基于跨域均值逼近的联合分布自适应轴承剩余使用寿命预测方法,首先,对轴承原始振动信号数据进行归一化处理;其次,通过投影矩阵将源域和目标域数据映射到一个低维公共特征子空间中,利用基于跨域均值逼近的联合分布自适应方法对源数据和目标轴承数据进行领域适配;最后,利用门控循环单元对轴承剩余使用寿命进行预测。在IEEE PHM Challenge 2012数据集上进行多组迁移实验,结果表明,所提方法在同一工况或不同工况下不同轴承间有良好的预测精度。

In recent years,deep learning provides a new method for predicting the remaining useful life of bearings.In practice,due to the small number of bearing degradation data and the large difference in the distribution of bearing data under different working conditions,it is hard to realize that the remaining useful life prediction model trained on one bearing,which can be used for other bearings remaining useful life prediction under the same or different working conditions.In order to deal with the aforementioned shortcomings,a joint distribution adaptation based on cross domain mean approximation for bearing remaining useful life prediction method is proposed.Firstly,the original vibration signal data of the bearing is normalized.Then the source domain and target domain data are projected to a corresponding lowdimensional common feature subspace by projection matrix.In the subspace,a joint distribution adaptation based on cross domain mean approximation method is used to perform domain adaptation for source and target data.Finally,the gated recurrent unit is used for the bearing remaining useful life prediction.The validity of the proposed method is demonstrated by experiments on IEEE PHM Challenge 2012 dataset.The results show that the proposed method has good prediction accuracy under the same working condition or different working conditions for different bearings.