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Cross-working-condition fault diagnosis method based on open set joint transfer learning

A technology of fault diagnosis and transfer learning, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as failure to guarantee the same failure modes of failure types and failure to achieve effective migration.

Active Publication Date: 2021-05-11
BEIHANG UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

However, in the actual fault diagnosis application scenario, there is no guarantee that the fault type of the target device to be diagnosed is exactly the same as the fault mode contained in the collected historical data set, and effective migration cannot be achieved.

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  • Cross-working-condition fault diagnosis method based on open set joint transfer learning
  • Cross-working-condition fault diagnosis method based on open set joint transfer learning
  • Cross-working-condition fault diagnosis method based on open set joint transfer learning

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specific Embodiment

[0102] In order to verify the effectiveness of the cross-working condition fault diagnosis method based on open set joint migration proposed by the present invention, the industrial gearbox dataset released in the 2009 PHM Data Challenge was used for case verification. The gearbox data set is collected from an industrial two-stage reduction gearbox, and an acceleration sensor is arranged at the input end of the gearbox to collect vibration data signals of the gearbox, such as Figure 5 shown. The number of teeth of the input shaft gear is 32, the number of teeth of the intermediate shaft gear is 48, and the first-stage reduction ratio is 1.5; the number of teeth of the output shaft gear is 80, and the second-stage reduction ratio is 1.667.

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Abstract

The invention discloses a cross-working-condition fault diagnosis method based on open set joint transfer learning. The method comprises the steps: training a feature extraction model and a feature classification model by extracting and recognizing the fault types of cross-working-condition source domain sample data and target domain sample data; building a fault diagnosis model by using the trained feature extraction model and feature classification model; and inputting target domain data needing fault diagnosis into the fault diagnosis model, and diagnosing a fault type corresponding to the target domain data.

Description

technical field [0001] The invention relates to a fault diagnosis method, in particular to a cross-working-condition fault diagnosis method based on open set joint transfer learning. Background technique [0002] Due to changes in load, speed and other factors during the actual operation of mechanical equipment, there are differences in operating conditions, and even the operating data of the same type of equipment will be different. In order to solve the problem that the fault diagnosis model cannot be trained due to insufficient training samples, the transfer learning method can use the operating data of similar equipment to realize the fault diagnosis of the target equipment lacking training data by minimizing the data difference of different working conditions. The existing fault diagnosis technology based on transfer learning usually uses domain feature transfer to assist in completing the fault diagnosis task of the target device under the premise that the fault types ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/048G06N3/045G06F18/24G06F18/214
Inventor 马剑尚芃超许庶王超丁宇吕琛
Owner BEIHANG UNIV
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