基于多尺度一维卷积神经网络(MS-1DCNN)的故障诊断方法研究,深度学习框架是pytorch。 西储大学故障诊断识别率为97.5%(验证集)以上!很好运行的 适用于刚上手故障诊断的同学,就是从数据处理,到最后出图可视化完整一套流程,看完这个会对故障诊断流程有个清晰认识。 数据集为凯斯西储大学轴承数据。
The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail ...
The left and right boundary points and peak points of the multifractal spectra of three-phase voltage signals in the fault data are extracted as features through multifractal theory, and the proposed features are input into a one-dimensional convolutional neural network (1DCNN) model for training....
The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail ...
The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail ...
The MS1DCNN is designed to extract fine-grained temporal features from packet-level data, whereas the WDTransformer leverages self-attention mechanisms to capture long-range dependencies and incorporates regularization techniques to mitigate overfitting. To further enhance performance on long-tail ...