As an important component of industrial equipment, once gears have failures, they may cause serious catastrophes. Thus, the prediction of gear remaining life is of great significance. The health indicator of gears is first generated by fusing time-domain and frequency-domain features of gears vibration signals via the isometric mapping algorithm. Then a new type of long-short-term memory neural network with weight amplification (LSTMP-A) is proposed for accurately predicting gear remaining life. Compared with traditional LSTMs, LSTMP-A amplifies the input weights and the recurrent weights of the hidden layer to different degrees by the attention mechanism according to the contribution degree of the corresponding data, and a projection layer is added into the network. With LSTMP-A, we can predict the health characteristics of gears based on historical fusion features. With the monitoring data of a gear life cycle test, the comparative experiments show that the proposed gear remaining life prediction method has higher prediction accuracy than the conventional prediction methods.

作为工业设备的重要组成部分，一旦齿轮发生故障，可能会造成严重的灾难。因此，齿轮剩余寿命的预测具有重要意义。首先通过等轴测映射算法融合齿轮振动信号的时域和频域特征来生成齿轮的健康指标。然后提出了一种新型的具有重量放大功能的长短期记忆神经网络（LSTMP-A），用于精确预测齿轮的剩余寿命。与传统LSTM相比，LSTMP-A通过关注机制根据相应数据的贡献程度将隐藏层的输入权重和循环权重放大到不同程度，并在网络中添加了投影层。使用LSTMP-A，我们可以根据历史融合特征预测齿轮的健康特征。通过齿轮寿命周期试验的监测数据，对比实验表明，提出的齿轮剩余寿命预测方法具有比常规预测方法更高的预测精度。