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Mechanical fault prediction method based on diversified integrated convolutional neural network

A technology of convolutional neural network and mechanical failure, applied in neural learning methods, biological neural network models, testing of mechanical components, etc., to achieve good applicability, improve accuracy and efficiency

Active Publication Date: 2020-07-10
CHINA UNIV OF GEOSCIENCES (WUHAN)
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  • Application Information

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However, the diversity strategies described above are all qualitative approaches

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  • Mechanical fault prediction method based on diversified integrated convolutional neural network
  • Mechanical fault prediction method based on diversified integrated convolutional neural network
  • Mechanical fault prediction method based on diversified integrated convolutional neural network

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[0038] In order to make the purpose, technical solution and advantages of the present invention clearer, the embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0039] Please refer to figure 1 , an embodiment of the present invention provides a structural diagram of a mechanical fault prediction method based on a diverse integrated convolutional neural network, specifically including:

[0040] S101: performing data preprocessing on mechanical fault vibration signals;

[0041] S102: improving the convolutional neural network based on the LeNet-5 model, and constructing a mechanical fault prediction model based on a diversified integrated convolutional neural network according to the improved convolutional neural network;

[0042] S103: Set the learning rate of the mechanical failure prediction model based on the diverse integrated convolutional neural network as a cyclic cosine learning rate mechanism, construct...

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Abstract

The invention provides a mechanical fault prediction method based on a diversified integrated convolutional neural network. The method comprises the steps of preprocessing data, constructing an integrated deep learning fault prediction model based on diversity indexes, and training the integrated deep learning fault prediction model based on diversity indexes. According to the invention, the learning rate of the model is set as a cyclic cosine learning rate mechanism, so that the local optimal value can be approached for multiple times in the training process, then search is continued throughhot restart, and meanwhile, in the model training stage, diversity indexes and diversity loss functions are constructed, so that the model is prompted to find a new local optimal value which is more different from the original local optimal value. Finally, the convolutional neural network models of all local optimal values are integrated. The method has the advantages that the fault prediction precision and efficiency are improved, the applicability is good, and popularization and use in practical application are facilitated.

Description

technical field [0001] The present invention relates to the related technical field of neural network fault prediction, in particular to a mechanical fault prediction method based on a diversified integrated convolutional neural network. Background technique [0002] Fault diagnosis plays an important role in modern equipment manufacturing industry. With the rapid development of smart manufacturing, equipment has become more and more complex, and the failure of these equipment may cause huge economic losses and even lead to dangerous situations. Fault diagnosis is a key technology to ensure the stability, reliability and safety of the above-mentioned equipment, and it is also a hot research topic in the academic and engineering circles. [0003] With the development of artificial intelligence (AI), deep learning (DL) has become a new paradigm in the field of machine learning. DL has data representation capabilities and can automatically extract data features, which can eli...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/00G01M13/045G06K9/00G06K9/62G06N3/04G06N3/08
CPCG01M13/00G01M13/045G06N3/08G06N3/045G06F2218/12G06F18/241
Inventor 文龙邓楚凡
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)