Rotary machinery fault diagnosis method under complex working condition based on meta transfer learning
A technology of transfer learning and complex working conditions, applied in the field of energy manufacturing, can solve problems such as model performance degradation, achieve the effects of reducing selection restrictions, reducing demand, improving accuracy and generalization performance
Active Publication Date: 2021-10-01
CHINA UNIV OF GEOSCIENCES (WUHAN)
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The invention discloses a rotary machinery fault diagnosis method under a complex working condition based on meta transfer learning, and the method comprises the steps of collecting original sensor signals of mechanical equipment in different states, and making an image data set; dividing the data set into a training set and a verification set; selecting a deep convolutional network as a pre-training model, and finishing training learning on ImageNet; using a meta-learning method to improve a parameter migration parameter initialization problem existing in migration learning, and obtaining parameter initialization optimization methods for multi-source domain and semi-supervised domain adaptive problems respectively; initializing a Meta-TCNN fault diagnosis model by using VGG-16 network parameters and adopting a meta learning optimization method; updating the Meta-TCNN parameters by adopting a fine tuning strategy; using the training set to train the Meta-TCNN model; stopping training until the final classification accuracy is not obviously improved any more; and verifying the trained Meta-TCNN model by using the verification set, and applying the model of which the parameters are completely optimized to a fault diagnosis task. The application range of the fault diagnosis method is expanded, and the cost is reduced.
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