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
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[0022] In order to make the objects, technical solutions, and advantages of the present invention, the embodiments of the present invention will be further described below in conjunction with the accompanying drawings.
[0023] Please refer to figure 1 The present invention provides a method of rotating mechanical fault diagnosis under complex working conditions based on metallographic learning, including the following steps:
[0024] S1, collect the original sensor signal of the mechanical equipment in different states, convert the one-dimensional raw signal to a two-dimensional time-frequency distribution image and then obtain the corresponding three-way time frequency image, as the fault diagnosis model in the present invention. Input image data set; please refer to figure 2 ,
[0025] The sample image is obtained by the original signal superposition. It is assumed that the sequence X is a set of vibration signal sequences. It is assumed that the picture size is N × m, then the...
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