Rolling bearing fault diagnosis method and system based on convolutional neural network

A convolutional neural network and rolling bearing technology, which is applied in the field of rolling bearing fault diagnosis and system based on convolutional neural network, can solve the problems of low detection efficiency and high misjudgment, and achieve the effect of improving accuracy and real-time performance

Inactive Publication Date: 2016-11-30
CHANGZHOU COLLEGE OF INFORMATION TECH
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  • Abstract
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  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to provide a rolling bearing fault diagnosis method and system to solve the technical problems of low efficiency and high misjudgment of traditional rolling bearing faults

Method used

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  • Rolling bearing fault diagnosis method and system based on convolutional neural network
  • Rolling bearing fault diagnosis method and system based on convolutional neural network
  • Rolling bearing fault diagnosis method and system based on convolutional neural network

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Embodiment 1

[0052] Such as figure 1 As shown, the present embodiment 1 provides a fault diagnosis method for rolling bearings, including the following steps:

[0053] Step S1, constructing a database for reflecting the working condition of the rolling bearing; and

[0054] Step S2, identifying the current working state of the rolling bearing through the database.

[0055] Such as figure 2 As shown, specifically, the method for constructing the database in step S1 is as follows:

[0056] Step S11, collecting the vibration signal of the rolling bearing during the working process;

[0057] Step S12, normalize the vibration signal; and

[0058] Step S13, creating a waveform database as the database according to the normalized vibration signal.

[0059] Further, the vibration signal is collected in the step S11, namely

[0060] Set the time sequence of the acceleration signal corresponding to the vibration signal as;

[0061] In the step S12, the vibration signal is normalized, that is...

Embodiment 2

[0083] On the basis of embodiment 1, this embodiment 2 also provides a rolling bearing fault diagnosis system, including: a database for storing data corresponding to the corresponding working status of the rolling bearing; and an identification module for identifying the current working status of the rolling bearing through the database.

[0084] For the construction of the database and the specific implementation steps of identifying the current working state of the rolling bearing through the database in the identification module, please refer to the relevant discussion of Embodiment 1.

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Abstract

The present invention relates to a rolling bearing fault diagnosis method and system based on a convolutional neural network. The method comprises the following steps: the step S1, constructing a database configured to reflect the work condition of a rolling bearing; and the step 2, identifying the work condition of the current rolling bearing through the database. The rolling bearing fault diagnosis method and system based on the convolutional neural network obtain the vibration signals of the rolling bearing through an accelerometer, and the fault diagnosis method of the rolling bearing employs the powerful image identification capability of the convolutional neural network model to identify faults of the rolling bearing through deep learning of big data so as to realize the monitoring and forecasting of the faults of the rolling bearing and substantially improve the accuracy and the timeliness of the rolling bearing fault diagnosis.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method and system based on a convolutional neural network. Background technique [0002] In industrial applications, rolling bearings are vulnerable parts as well as critical monitoring components, and thus require fault diagnosis. Taking the rolling bearing of a wind turbine as an example, due to the low speed of the main shaft of the direct drive wind turbine, the fault characteristic frequency of the rolling bearing is in a low frequency band, and the early fault characteristics are relatively weak. In addition, due to large changes in wind speed, wind turbines generally work under variable speed conditions, which increases the difficulty of rolling bearing fault diagnosis. [0003] Therefore, in order to solve the technical problems of difficult and high misjudgment of rolling bearing fault diagnosis, it is necessary to design a new rolling bearing fault diagnosis method and system. Cont...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/04
CPCG01M13/045
Inventor 王二化朱俊赵黎娜
Owner CHANGZHOU COLLEGE OF INFORMATION TECH
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