联合故障机理和卷积神经网络的齿轮剩余使用寿命预测方法研究

Research on the Prediction Method of Remaining Useful Life of Gears by Combining Fault Mechanism and Convolutional Neural Network

齿轮在疲劳退化过程中通常伴有平稳型故障和冲击型故障,这两类故障特征包含了丰富的齿轮退化信息。目前端到端的剩余使用寿命预测方法大多以神经网络提取的语义特征为基础,预测模型的建立与齿轮关联程度较低,物理解释性较差。为此,结合平稳型、冲击型故障振动响应信号模型和卷积神经网络,依据故障信号特征参数生成融合故障先验的时域、角域卷积核,利用神经网络的特征提取能力挖掘振动信号中的平稳型、冲击型故障成分;同时引入注意力机制,抑制噪声、动态捕捉不同故障成分以及不同时刻信号中的退化信息,最后利用模型噪声协方差矩阵实现预测误差的加权优化。仿真和试验结果表明,该方法能有效挖掘原始信号的故障信息,具备较好的物理解释性,且对比传统智能预测方法具有更高的剩余使用寿命预测精度和更好的鲁棒性,验证了所提方法的有效性。

In the process of fatigue degradation of gears,there are usually steady-type faults and shock-type faults.These two types of fault features contain rich information about gear degradation.Most of the current end-to-end prediction methods on remaining useful life are based on the semantic features extracted by neural network.Basically,the establishment of the prediction model has a low degree of correlation with the gear,and the physical interpretability is poor.To this end,combined with the steady-type and shock-type fault response mechanism and the convolutional neural network,a convolution kernel in time domain and angle domain fused with fault prior is designed,and the feature extraction ability of the neural network is used to mine the steady-type and shock-type faults in the vibration signal.At the same time,an attention mechanism is introduced to suppress noise,dynamically capture different fault components and degraded information in signals at different times.Finally,the weighted optimization of the prediction error is realized by the noise covariance matrix of the model.The simulation and experimental results show that the method can effectively mine the fault information of the original signal,has better physical interpretability,and has higher prediction accuracy and better robustness on remaining useful life than the traditional intelligent prediction method,which verifies the effectiveness of the proposed method.