改进Alexnet的滚动轴承变工况故障诊断方法

旋转机械中的滚动轴承常工作在变负荷、强噪声的环境中，而传统的滚动轴承故障诊断方法难以在复杂工况下自适应地提取对其故障诊断有利的特征，针对此问题，提出一种改进AlexNet的滚动轴承变工况故障诊断方法。首先，将采集的一维时域信号按横向插样构建便于改进AlexNet输入的二维特征图，于现存的纵向插样和二维频谱而言，保留了特征自动提取过程中振动信号的时序性和关联性；其次，改进调整AlexNet卷积层的功能层且经过卷积和次采样等操作，从二维特征图中自动提取出利于滚动轴承状态辨识的特征；最后，以softmax的交叉熵为损失函数，利用Adam按小批量迭代优化法实现对滚动轴承故障的诊断。通过与多种方法对滚动轴承不同位置、不同损伤程度的12类状态诊断效果比较，结果表明,该方法对变负荷、强噪声条件下的滚动轴承故障诊断的精度更高，鲁棒性更强。

Improved Alexnet Based Fault Diagnosis Method for Rolling Bearing Under Variable Conditions

Rolling bearings in rotating machinery often work in the environment with variable loads and strong noise. Traditional fault diagnosis methods of rolling bearings are difficult to adaptively extract the favorable features under complex conditions, so a fault diagnosis method of rolling bearings with variable conditionsis proposedbased on improved AlexNet. Firstly, one-dimensional time-domain signals are translated into two-dimensional feature maps using transverse insert samples method to satisfy the requirements of the improved AlexNet input.Compared with the existing longitudinal insert samples method or two-dimensional spectrums method, the time series and correlation of vibration signals are preserved during feature extraction automatically. Secondly, the functional layer of AlexNet convolutional layer is improved and adjusted, andthe profitable characteristics for the state identification of rolling bearingscanbe automatically extracted via the convolution and sampling operations of improved AlexNet from the two-dimensional feature maps. Finally, the softmax cross entropy is considered as a loss function and Adam is used to realize the fault diagnosis of rolling bearings according to a small batch iterative optimization method. Compared the diagnosis effects with other methods for 12 kinds of states of different positions and damage degrees of rolling bearings under variable loads and strong noise, the results show that the proposed method has a higher accuracy of fault diagnosis of rolling bearing and its robustness is stronger.