基于概率切片累积特征的轴承双向传感器信息融合故障诊断

Fault diagnosis of bearing bidirectional sensor information fusion based on probability slice cumulative characteristics

针对采集的轴承振动信号易受环境的影响而导致存在许多不确定性因素的现实情况,采用一种基于概率切片累积特征的轴承双向传感器信息融合故障诊断方法实现对轴承故障的定性分析。首先利用概率盒理论(P-box)将来自水平和垂直方向传感器的时域信号分别进行概率盒建模,从而减小认知不确定性带来的消极影响并充分提取多方位振动信号中故障信息;然后提取模型概率切片累积特征输入到构建的双通道并行卷积神经网络(PCNN)自适应训练,在此基础上通过在网络的全连接层之前添加一个融合层进行双向特征信息融合;最后利用归一化指数函数实现故障部位的辨识。某铁路局机务段轮对轴承数据分析结果表明,所采用方法在应对故障程度不均衡数据集时仍具有较高的准确性和稳定性,且在不同噪声条件下具有一定的鲁棒性。

Since the collected bearing vibration signals are susceptible to many uncertainties caused by the environment,a fusion fault diagnosis method based on probabilistic slicing cumulative features of bi-directional sensor information of bearings was used for the qualitative analysis of bearing faults.Probability box theory(P-box)method was applied to model the time domain signals from horizontal and vertical sensors separately to reduce the negative impact of cognitive uncertainty and fully characterize the multi-directional fault vibration information.Further,the probability box was divided into focal element slices to extract the model probability slice cumulative feature matrix.Subsequently,a dual-channel Parallel Convolutional Neural Network(PCNN)was established to fuse the bi-directional feature information by constructing a fusion layer before the fully connected layer of the network.A normalized exponential function was an input to achieve the fault site identification.The analysis results of wheel-to-wheel bearing data of a railroad bureau showed that the proposed method had high accuracy and stability when dealing with imbalanced data sets of fault degree and had certain robustness under different noise conditions.