Lanzhou University of Technology
近年来，许多基于深度学习的方法被用于故障诊断领域，并且取得了良好的效果，但是发电机故障样本数据难以获取,在数据量较少的情况下，基于深度学习的方法存在过拟合现象，导致模型泛化能力差、诊断精度不高。为了解决这一问题，本文提出一种基于随机变分推理贝叶斯神经网络的故障诊断方法，该方法以贝叶斯推理与随机变分推理为基础，可以根据少量数据得到较为可靠的模型，获得网络各层参数的概率分布，可以有效解决过拟合的问题。采用证据下限（Evidence Lower Bound，ELBO）派生类函数TraceGraph ELBO进行随机变分推理，解决派生类函数Trace ELBO诊断精度较低问题。将该方法应用于发电机轴承的故障诊断，并与其他方法对比，结果表明，该方法在故障样本数据量较少的情况下具有较高的诊断性能。
In recent years, many deep learning-based methods have been used in the field of fault diagnosis and have achieved good results. However, it is difficult to obtain generator fault sample data. In the case of a small amount of data, deep learning-based methods have over-fitting phenomenon, resulting in poor model generalization ability and low diagnostic accuracy. In order to solve this problem, this paper proposes a fault diagnosis method based on random variational inference Bayesian neural network, this method is based on Bayesian inference and random variational inference, which can obtain a more reliable model based on a small amount of data. Obtaining the probability distribution of the parameters of each layer of the network can effectively solve the problem of overfitting. The Evidence Lower Bound derived function TraceGraph ELBO is used to carry out random variational inference, which solves the problem of low diagnostic accuracy of the derived function Trace ELBO. This method is applied to the fault diagnosis of generator bearings and compared with other methods. The results show that this method has higher diagnostic performance when the amount of fault sample data is small.