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A multi-feature health factor fusion method based on sdrsn

A technology of health factors and fusion methods, applied in neural learning methods, testing of mechanical components, testing of machine/structural components, etc., to reduce the influence of empirical factors, remove redundant information, and avoid overfitting.

Active Publication Date: 2022-07-29
HARBIN INST OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to propose a multi-feature health factor fusion method based on SDRSN in view of the problem that the existing technology cannot guarantee that the activation value is transmitted between the various layers of the network in a normalized state and avoid over-fitting phenomenon

Method used

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  • A multi-feature health factor fusion method based on sdrsn
  • A multi-feature health factor fusion method based on sdrsn
  • A multi-feature health factor fusion method based on sdrsn

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specific Embodiment approach 1

[0051] Specific implementation mode 1: refer to figure 1 and figure 2 Specifically describe this embodiment, a multi-feature health factor fusion method based on SDRSN described in this embodiment is characterized by comprising:

[0052] Step 1: Collect the original vibration signal of the rotating machinery;

[0053] Step 2: perform smoothing and denoising preprocessing on the original vibration signal of the rotating machinery, and then extract the time domain, frequency domain and time-frequency domain features of the preprocessed original vibration signal of the rotating machinery, and construct the original feature set, Then normalize the signal in the original feature set;

[0054] Step 3: Use the normalized original feature set to filter and construct a sensitive feature set;

[0055] Step 4: Input the sensitive feature set into the SDRSN model for feature fusion training, input the data of the test set into the trained model, and obtain the health factor representi...

specific Embodiment approach 2

[0065] Embodiment 2: This embodiment is a further description of Embodiment 1. The difference between this embodiment and Embodiment 1 is that the final output of the model is expressed as:

[0066] x l+1 =x l +F(x l ,W l )

[0067] where x l Represents the output feature A, F(x l ,W l ) represents the output feature B.

specific Embodiment approach 3

[0068] Embodiment 3: This embodiment is a further description of Embodiment 2. The difference between this embodiment and Embodiment 2 is that the output feature of the convolutional layer is expressed as:

[0069] y 1 =∑x*k+b

[0070] where x is the input feature, k is the convolution kernel, and b is the bias.

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Abstract

A multi-feature health factor fusion method based on SDRSN, which relates to the technical field of fault prediction. In view of the problem that the model in the prior art cannot reduce the influence of empirical factors and remove redundant information, the SDRSN model can perform adaptive feature learning, according to attention The force mechanism finds the interference features of the input samples, and uses the soft threshold function to set them to zero, thereby reducing the influence of interference factors on the feature mining effect. Compared with traditional feature fusion methods, this model can reduce the influence of empirical factors and remove redundant information. The idea of ​​self-normalization is introduced into the SDRSN model, which can ensure that the activation value is transmitted between the layers of the network in a normalized state, avoid the occurrence of overfitting, and obtain features containing rich information, so as to better represent the rotation. Mechanical health.

Description

technical field [0001] The invention relates to the technical field of fault prediction, in particular to a multi-feature health factor fusion method based on SDRSN. Background technique [0002] Due to its versatility, rotating machinery is currently widely used in various mechanical equipment and complex working environments. Once damaged, it will not only affect the normal use of the equipment, but also may cause huge economic losses and personal safety threats. Therefore, it has always been an urgent need in the field of machinery health monitoring to carry out research on rotating machinery fault prediction methods, and constructing an effective health factor is a prerequisite for accurate prediction of rotating machinery faults. [0003] Self-Normalizing Neural Networks (SNN) uses Scaled Exponential Linear Units (SELU) as the activation function, which can ensure that the activation value is transmitted between layers of the network in a normalized state, and This val...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/00G01H17/00G06K9/62G06N3/08
CPCG01M13/00G01H17/00G06N3/08G06F18/253
Inventor 杨京礼高天宇姜守达
Owner HARBIN INST OF TECH
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