Domain adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning

A domain self-adaptive and fault diagnosis technology, applied in the field of bearing fault diagnosis, can solve problems such as inconsistency of source domain data sets, and achieve the effect of reliable classification, narrowing differences and avoiding losses

Pending Publication Date: 2022-04-12
SHANDONG UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0007] In order to solve the deficiencies of the prior art, the present invention provides a domain-adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning, which considers the inconsistency between the source domain data set and the target domain data set, and combines the sparse autoencoder , when the data samples are insufficient, the known characteristic signals are used to classify the unknown fault diagnosis with high precision, which ensures the safe operation of mechanical equipment

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  • Domain adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning
  • Domain adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning
  • Domain adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning

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Embodiment 1

[0044] Such asfigure 1 As shown, Embodiment 1 of the present invention provides a domain-adaptive bearing voiceprint fault diagnosis method based on reinforcement learning. In this embodiment, the method is applied to a server for illustration. A terminal may also be applied to include a terminal, a server and a system, and may be realized through interaction between the terminal and the server. The terminal and the server may be connected directly or indirectly through wired or wireless communication, which is not limited in this application.

[0045] The method includes the following processes:

[0046] Obtain the voiceprint signal of the rolling bearing to be tested;

[0047] According to the obtained rolling bearing voiceprint signal and fault diagnosis model, the fault diagnosis result is obtained;

[0048] Among them, the loss function of the fault diagnosis model is the sum of the loss function of the domain adaptive network and the loss function of the classification...

Embodiment approach

[0073] As one or more implementations, after the training of the fault diagnosis network, the prediction result of the target domain can be obtained by inputting the target domain, and the characteristics and prediction results of the target domain are applied to the optimization of the fault diagnosis model at the same time, and the loss of the classification network is changed The function performs reinforcement learning on the fault diagnosis model.

[0074] Specifically, the obtained training results of the target domain have rich information, which is used as the label of the test data and the output of the test data feature extraction network as the input of the classification network, and the obtained loss function is added to the original loss function. , formula (9) is the calculation formula:

[0075]

[0076] where Y is the training dataset label, is the output of the training data classification network, Y tt is the spurious label of the test dataset, is th...

Embodiment 2

[0083] Embodiment 2 of the present invention provides a domain-adaptive bearing voiceprint fault diagnosis system based on reinforcement learning, including:

[0084] The data acquisition module is configured to: acquire the voiceprint signal of the rolling bearing to be tested;

[0085] The fault diagnosis module is configured to: obtain a fault diagnosis result according to the acquired voiceprint signal of the rolling bearing and the fault diagnosis model;

[0086] Among them, the loss function of the fault diagnosis model is the sum of the loss function of the domain adaptive network and the loss function of the classification network, the input of the classification network is the source domain label and the source domain output of the feature extraction network, and the input of the domain adaptive network is The source domain output and the target domain output of the feature extraction network; the source domain data is sequentially passed through the sparse autoencode...

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Abstract

The invention provides a domain adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning, and the method comprises the steps: obtaining a fault diagnosis result according to an obtained rolling bearing voiceprint signal and a fault diagnosis model; wherein the loss function of the fault diagnosis model is the sum of the loss function of the domain adaptive network and the loss function of the classification network, the input of the classification network is the source domain label and the source domain output of the feature extraction network, and the input of the domain adaptive network is the source domain output and the target domain output of the feature extraction network; source domain output is obtained after the source domain data sequentially passes through the sparse auto-encoder and the feature extraction network, and target domain output is obtained after the target domain data passes through the feature extraction network; according to the method, the problem that a source domain data set and a target domain data set are inconsistent is considered, the sparse auto-encoder is combined, when data samples are insufficient, unknown fault diagnosis is subjected to high-precision classification through known feature signals, and safe operation of mechanical equipment is guaranteed.

Description

technical field [0001] The invention relates to the technical field of bearing fault diagnosis, in particular to a domain-adaptive bearing voiceprint fault diagnosis method and system based on reinforcement learning. Background technique [0002] The statements in this section merely provide background art related to the present invention and do not necessarily constitute prior art. [0003] In today's industrial production process, the normal operation status of mechanical equipment is the main factor restricting production efficiency. Rotating machinery is a kind of industrial mechanical equipment and is widely used. Diagnosing the health of rolling bearings as its key components is of great significance. Due to the continuity of the working state of mechanical equipment and the harshness of the working environment, rolling bearing failures occur from time to time. Therefore, fault diagnosis before accidents can improve the reliability of industrial production. [0004]...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/00G06Q50/04G06K9/62G06N3/04G06N3/08
CPCY02P90/30
Inventor 姜明顺刘明慧张艺蓝张法业张雷隋青美
Owner SHANDONG UNIV
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