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Bearing fault classification system and method based on multi-scale domain adaptive network

An adaptive network and fault classification technology, applied in the field of automation, can solve the problem of no model, guidance, etc., and achieve the effect of reducing distribution differences, clear functions, and labor saving

Pending Publication Date: 2022-05-27
SHAANXI COAL IND GRP SHENMU NINGTIAOTA MINING CO LTD +1
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

Most algorithms directly use raw data as input, extract features through intelligent algorithm "black box" learning, and do not use domain knowledge to guide the model correctly

Method used

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  • Bearing fault classification system and method based on multi-scale domain adaptive network
  • Bearing fault classification system and method based on multi-scale domain adaptive network
  • Bearing fault classification system and method based on multi-scale domain adaptive network

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Embodiment Construction

[0075] In order to further understand the present invention, the present invention will be described below in conjunction with the embodiments. These descriptions are only used to further explain the features and advantages of the present invention, and are not intended to limit the claims of the present invention.

[0076] The invention designs a multi-scale depth domain self-adaptive intelligent algorithm model combined with prior knowledge for bearing fault diagnosis and classification. The model structure is as follows figure 1 shown. It should be noted that the present invention analyzes the fault diagnosis of the bearing, and the main common faults are the bearing inner ring fault, outer ring fault and rolling element fault, plus the normal data, the model faces a four-classification problem; In the adaptive algorithm, the source domain refers to the dataset with labeled fault classification information, and the target domain refers to the unlabeled fault information dat...

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Abstract

According to the bearing fault classification system and method based on the multi-scale field adaptive network, aiming at the conditions of label information loss and data distribution offset in the actual industry, field knowledge is combined and fused into the model, the existing label data resources can be fully utilized, and the bearing fault classification efficiency is improved. When the equipment data distribution deviates due to the working environment, the multi-equipment multi-working-condition label data knowledge can still be migrated to the equipment needing to be diagnosed, so that the problem of label missing in real industrial production is solved to a great extent, the use value of the label is improved to a great extent, and meanwhile, the bearing fault diagnosis efficiency is also improved.

Description

technical field [0001] The invention belongs to the field of automation and relates to bearing fault diagnosis, in particular to a bearing fault classification system and method based on a multi-scale domain self-adaptive network. Background technique [0002] With the continuous development of modern scientific and technological productivity and the continuous progress of information technology, the demand for low-cost, high-quality products and production safety in modern industry is increasing day by day. The maintenance of rotating equipment in industrial production is rapidly shifting from preventive maintenance to rotating Real-time monitoring of equipment status and intelligent fault diagnosis. Rotating machinery is one of the most widely used types of rotating equipment. It is very important in industrial production and has an irreplaceable position. At the same time, rolling bearings are one of the most important components in rotating machinery, which has an import...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06F17/18G01M13/045
CPCG06N3/08G06F17/18G01M13/045G06N3/045G06F18/2431G06F18/214Y02P90/80
Inventor 谭震韩磊刘美乐曹振辉朱信龙景少波陈菲梁少剑关欣杰
Owner SHAANXI COAL IND GRP SHENMU NINGTIAOTA MINING CO LTD