基于矩阵乘积态的机械故障诊断方法研究

Research on Mechanical Fault Diagnosis Method Based on Matrix Product State

在机械故障诊断中,针对传统神经网络处理高阶数据难度大、网络参数多、耗费大量计算资源的不足,提出了一种基于矩阵乘积态的张量网络故障诊断方法。通过输入高阶张量故障数据到矩阵乘积态故障诊断模型中,将高阶张量表示为多个低阶张量,从而简化数据结构和参数量。为了验证该方法的有效性,将其应用在齿轮的故障诊断中,并与传统的卷积神经网络故障诊断模型进行对比。同时,验证了键维度对模型准确率的影响。结果表明:所提模型的键维度会影响模型准确率,键维度为16的模型准确率高于键维度为8的模型准确率;该模型在减小数据复杂度的同时,还可以识别不同故障类型,准确率达到90%,比传统的卷积神经网络故障诊断模型性能更好。

In mechanical fault diagnosis,it is difficult for traditional neural networks to process high-level data,and many network parameters consume a lot of computing resources.Therefore,this paper proposes a tensor network fault diagnosis method based on matrix product state.By inputting high-order tensor fault data into the matrix product state fault diagnosis model,the high-order tensor is represented as multiple low-order tensors,thus simplifying the data structure and reducing the parameter number.In order to verify the effectiveness of the method,it is applied to the fault diagnosis of gears and compared with the traditional convolutional neural network fault diagnosis model.Moreover,the effect of bond dimension on the accuracy of the model was assessed.The experimental results show that the bond dimension of the proposed model affects the model accuracy,demonstrating a higher accuracy when the bond dimension is 16 in comparison to that of the model with a bond dimension of 8.While reducing the data complexity,the model can also identify different fault types with an accuracy of about 90%,which outperforms the traditional convolutional neural network fault diagnosis model.