轴承是旋转机械设备中的重要部件，由于工况、材质、加工方式等原因，轴承寿命时长相差许多。传统的并行或串行神经网络预测方式，对数据集有一定要求。因此，需要一种能够适用于不同数据长短的轴承剩余使用寿命预测网络。为此提出了一种能够预测不同寿命时长的Transformer-LSTM串并行神经网络预测模型。通过将Transformer解码层进行重构，并与长短期记忆时序神经网络（long short-term memory, LSTM）网络结构融合，实现轴承寿命数据的串并行预测处理。实验结果表明Transformer-LSTM神经网络能够精准预测长、中、短不同寿命时长的轴承失效时间，具有较强的模型泛化能力，提升轴承寿命预测精度与模型的泛化能力。
Abstract
Bearing is an important component in rotating machinery. Due to the working conditions, materials, and processing methods, the lifetimes of bearing have large fluctuations. Traditional parallel or serial neural network prediction methods have seriously dependent on the data sets. Therefore, there is a need for bearings RUL prediction network that can be applied to different data lengths. To overcome this challenge, a Transformer-LSTM serial-parallel neural network prediction model is proposed, which can predict the RUL for bearings with different lifetimes. By reconfiguring the Transformer decoding layer and fusing it with the LSTM network structure, the serial-parallel prediction processing of bearing life data is achieved. The experimental results show that the Transformer-LSTM neural network can accurately predict the bearing failure time for different lifetimes, including: long, medium, and short. Moreover, the model has a stronger generalization ability which also indicates that the proposed method can improve the prediction accuracy of bearing life.
关键词
滚动轴承 /
轴承寿命预测 /
Transformer神经网络 /
LSTM神经网络 /
非线性时间序列预测
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Key words
rolling bearings /
bearing remaining useful life prediction /
Transformer neural network /
LSTM neural network /
Non-linear time series forecasting
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