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

A rotating machinery and dynamic technology, applied in the field of machinery, can solve the problems of low prediction accuracy and poor generalization ability of remaining life prediction of rotating machinery, and achieve the effect of improving model prediction accuracy and generalization ability.

Active Publication Date: 2021-05-07
XIDIAN UNIV
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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 bas

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

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Embodiment Construction

[0058] The present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments.

[0059] refer to figure 1 , the implementation steps of the present invention are further described in detail.

[0060] Step 1, construct a multi-layer convolutional neural network.

[0061] Build a 19-layer convolutional neural network consisting of four sub-modules with the same structure connected in series and then connected to the fully connected layer and the output layer. The structure of each sub-module consists of a convolutional layer, an activation function layer, a normalization layer and Pooling layer composition.

[0062] Set the parameters of each layer in the sub-module as follows: set the number of convolution kernels of the convolution layer in the first to fourth sub-modules to 4, 8, 16, and 32 respectively, and set the size of the convolution kernel to 3×1. The product step size is set to 2; the activation functi...

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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...

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Application Information

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IPC IPC(8): G06F30/27G06K9/00G06K9/62G06N3/04G06N3/08G06F119/04
CPCG06F30/27G06N3/084G06F2119/04G06N3/043G06N3/048G06F2218/08G06F2218/12G06F18/241
Inventor 王奇斌孔宪光程涵徐元兵杨胜康徐锟
Owner XIDIAN UNIV
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