Semi-supervised mechanical fault diagnosis method based on adaptive migration neural network

A neural network, mechanical failure technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as not easy to implement

Pending Publication Date: 2021-01-05
SOUTH CHINA UNIV OF TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, it is necessary to re-collect labeled fault data for each diagnosis, which is not easy to achieve in practical applications. Therefore, it is necessary to find an effective learnin

Method used

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  • Semi-supervised mechanical fault diagnosis method based on adaptive migration neural network
  • Semi-supervised mechanical fault diagnosis method based on adaptive migration neural network
  • Semi-supervised mechanical fault diagnosis method based on adaptive migration neural network

Examples

Experimental program
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Example Embodiment

[0065] Example 1

[0066] This embodiment discloses a semi-supervised mechanical fault diagnosis method based on an adaptive migration neural network. The method uses a deep neural network G and an independent classifier C to learn on historical fault data and transfer the learned knowledge to the target task, thereby improving the diagnostic performance. like figure 1 As shown, the steps are as follows:

[0067] S1. Data acquisition and calibration: According to the actual mechanical equipment fault diagnosis task, use the sensor to obtain the source domain fault data of the corresponding mechanical equipment, so as to obtain multiple source domain fault data sets{X s , Y s}, where the source domain fault data set consists of the source domain fault training sample X obtained by intercepting the source domain fault data s and its corresponding label Y s Composition, the label is the fault type corresponding to the sample. Each fault data is intercepted based on the faul...

Example Embodiment

[0113] Example 2

[0114] This embodiment discloses a semi-supervised mechanical fault diagnosis device based on an adaptive migration neural network, such as Figure 5 shown, including:

[0115] The data acquisition and calibration module is used to obtain the source domain fault data of the corresponding mechanical equipment by using the sensor according to the actual mechanical equipment fault diagnosis task, so as to obtain multiple source domain fault data sets{X s , Y s}, where the source domain fault data set consists of the source domain fault training sample X obtained by intercepting the source domain fault data s and its corresponding label Y s Composition, the label is the fault type corresponding to the sample; according to different mechanical equipment, or the target domain fault data obtained under different working conditions or positions of the same mechanical equipment, construct the target domain fault data X without labels t The target domain fault dat...

Example Embodiment

[0123] Example 3

[0124] This embodiment discloses a storage medium, which stores a program. When the program is executed by a processor, the semi-supervised mechanical fault diagnosis method based on the adaptive migration neural network described in Embodiment 1 is implemented, specifically as follows:

[0125] S1. Data acquisition and calibration: According to the actual mechanical equipment fault diagnosis task, use the sensor to obtain the source domain fault data of the corresponding mechanical equipment, so as to obtain multiple source domain fault data sets{X s , Y s}, where the source domain fault data set consists of the source domain fault training sample X obtained by intercepting the source domain fault data s and its corresponding label Y s Composition, the label is the fault type corresponding to the sample; according to different mechanical equipment, or the target domain fault data obtained under different working conditions or positions of the same mechani...

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Abstract

The invention discloses a semi-supervised mechanical fault diagnosis method based on an adaptive migration neural network, and the method comprises the steps: firstly obtaining a plurality of source domain fault data sets composed of source domain fault training samples and corresponding tags, and a plurality of target domain fault data sets composed of target domain fault data without tags, wherein the target domain fault data is divided into a target domain fault training sample and target domain fault test data; normalizing the data; constructing an adaptive migration neural network diagnosis model, supervising the training model and constructing a classifier loss function by using the source domain fault data set, constructing a classifier discrimination loss function, and performing adversarial training on the feature extractor and the classifier by using the target domain fault training sample; inputting the target domain fault test data into the trained model, and summing and averaging the two output probability values to obtain a final classification diagnosis result. The method can improve the discrimination capability of the fault data of the target domain, and effectively improves the intelligent fault diagnosis task under the actual variable working condition.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a semi-supervised mechanical fault diagnosis method based on an adaptive migration neural network. Background technique [0002] Rotating machinery is widely used in aerospace, automobile manufacturing, rail transit, wind power generation and other important engineering fields of national economy and people's livelihood, and plays a pivotal role in national economic production. Carry out condition monitoring and diagnosis of mechanical equipment, detect, diagnose and predict possible failures, so as to "prevent them before they happen", and ensure the reliable, continuous and stable operation of machinery, reduce economic losses and operating costs, and avoid major accidents , has very important practical needs and practical significance. [0003] The diagnosis method based on machine learning does not need to establish a dynamic model of complex components or...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/24G06F18/214
Inventor 李巍华钟琪陈祝云
Owner SOUTH CHINA UNIV OF TECH
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