Mechanical equipment key part residual life prediction method combining AE and bi-LSTM
A technology for life prediction and mechanical equipment, applied in neural learning methods, geometric CAD, biological neural network models, etc., can solve the problems of difficult feature extraction and low accuracy of remaining life prediction
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[0045] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are part of the embodiments of the present invention, rather than All the embodiments; based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative work all belong to the protection scope of the present invention.
[0046] like figure 1As shown, the embodiment of the present invention provides a method for predicting the remaining life of key parts of mechanical equipment combining AE and bi-LSTM, including the following steps:
[0047] S1. Extract features from the input data through an autoencoder;
[0048] First, in order to avoid the influence of noise on the data analysis results, the original data is denoised; then, the ...
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