Method for predicting residual life of rotating machinery under multiple working conditions based on dynamic domain adaptation network

Abstract

The invention discloses a method for predicting the residual life of a rotating machinery under multiple working conditions based on a dynamic domain adaptation network. The method comprises the following steps: 1, generating a source domain sample set and a target domain sample set; 2, preprocessing vibration signals in the source domain sample set and the target domain sample set; 3, generating a target domain training set and a target domain test set; 4, selecting a source domain training set by adopting a reverse verification technology; 5, constructing a dynamic domain adaptive neural network which structurally comprises a feature extractor, a prediction learning module, a marginal distribution adaptive module and a conditional distribution adaptive module; 6, training the dynamic domain adaptive neural network to obtain a trained dynamic domain adaptive neural network model; and 7, predicting the residual life of a target domain test set by using the model. According to the method, the generalization ability and the prediction precision of the residual life prediction model are improved under the condition of multiple working conditions.

Description

technical field [0001] The invention belongs to the technical field of machinery, and further relates to a method for predicting the remaining life of a multi-working-condition rotating machinery based on a dynamic domain adaptation network in the technical field of rotating machinery. The invention can be used to predict the remaining life of the rotating machinery under the condition of multiple working conditions. Background technique [0002] As a key component in mechanical equipment, rotating machinery affects the reliable operation of mechanical equipment. Therefore, it is extremely important to accurately predict the health status of rotating machinery in the operation and maintenance management of mechanical equipment. The current commonly used forecasting methods are based on data-driven forecasting methods. Traditional data-driven methods are highly sensitive to degraded features, require the support of expert experience, and have poor self-adaptability. Althou...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems in the above-mentioned prior art, and provide a multi-condition rotating machinery residual life prediction method based on dynamic domain adaptation network, which is used for Solve the problems of poor generalization ability and low prediction accuracy of remaining life prediction of rotating machinery

Images