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Rolling bearing fault diagnosis method and system based on domain migration and storage medium

A technology for fault diagnosis and rolling bearings, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as difficult to obtain ideal diagnostic results, achieve good generalization ability, high accuracy rate, and improve diagnostic accuracy.

Pending Publication Date: 2021-02-26
BEIJING INFORMATION SCI & TECH UNIV
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Problems solved by technology

[0004] Aiming at the above-mentioned problem that the current fault diagnosis based on deep learning requires massive training data, and it is difficult to obtain ideal diagnostic results under a small amount of sample data, the purpose of the present invention is to provide a rolling bearing fault diagnosis method, system and storage medium based on domain migration , which can better intelligently identify various types of faults and has a high diagnostic accuracy

Method used

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  • Rolling bearing fault diagnosis method and system based on domain migration and storage medium
  • Rolling bearing fault diagnosis method and system based on domain migration and storage medium
  • Rolling bearing fault diagnosis method and system based on domain migration and storage medium

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Embodiment

[0046] In order to verify the effectiveness of the method of the present invention, the public experimental data set of Case Western Reserve University was selected as the experimental data for training and testing. (N), inner ring fault (IF), outer ring fault (OF) and ball fault (BF); each fault location contains three fault sizes, which are 0.18mm, 0.36mm and 0.54mm respectively. According to different working conditions, the data is divided into 4 domains, and the data in each domain is divided into 10 health states according to different health states and fault sizes. The identifications are shown in Table 4.

[0047] Table 4 Bearing Status Identification

[0048]

[0049] Since the original vibration signal provided in the data set is a very long one-dimensional data, the number of samples of each data is relatively small. In order to obtain as many samples as possible, the following methods are used: figure 2 The sliding window shown is overlapped sampling, in order...

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Abstract

The invention relates to a domain migration-based rolling bearing fault diagnosis method and system and a storage medium. The method comprises steps of constructing a domain migration deep learning network; and inputting the source domain data and the target domain data into a domain migration deep learning network together, and training the network so that the domain to which the features belongcannot be distinguished while the extracted features are subjected to fault classification. According to the method, various fault types can be better and intelligently identified, and the method hashigh diagnosis accuracy. The method can be widely applied to the technical field of mechanical fault diagnosis.

Description

technical field [0001] The invention relates to the technical field of mechanical fault diagnosis, in particular to a domain migration-based rolling bearing fault diagnosis method, system and storage medium. Background technique [0002] Bearing is the most important part of rotating machinery, its main function is to support the rotating body of the machine and reduce the friction coefficient during motion. However, the constant wear created by the relative motion between the mating surfaces can lead to component damage. Therefore, it is an urgent problem to be solved and one of the most challenging tasks to study an effective bearing health state fault diagnosis method and detect the fault type. Traditional methods generally use signal processing methods to extract features, such as time-domain analysis, frequency-domain transform, wavelet transform, and envelope demodulation algorithms. But they all have their own deficiencies, including but not limited to time-domain a...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/044G06N3/045G06F2218/08G06F2218/12G06F18/214
Inventor 谷玉海吴洛冰朱腾腾王少红
Owner BEIJING INFORMATION SCI & TECH UNIV
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