Fault diagnosis method based on deep convolution domain adversarial transfer learning

Abstract

The invention discloses a fault diagnosis method based on deep convolution domain adversarial transfer learning, and the method comprises the steps: carrying out high-level feature extraction in a DCDATL through employing a deep convolution residual feature extractor, and improving the convergence and nonlinear approximation capability of the DCDATL; acquiring feature joint distribution representation through an obtained Kronecker product of high-level features and label information and is embedded into a domain classifier, and training domain adversarial to improve the migration performance of the DCDATL. The feature migration and classification process of the joint distribution domain confrontation overall loss function based on the minimized DCDATL improves the classification precisionafter migration. Due to the advantages of the DCDATL, the fault diagnosis method based on the DCDATL can perform high-precision fault diagnosis on the current to-be-detected sample of the rotating machine by utilizing the labeled sample under the historical working condition under the condition that the labeled sample under the current working condition of the rotating machine does not exist.

Description

technical field [0001] The invention belongs to the technical field of fault diagnosis methods for rotating machinery, and in particular relates to a fault diagnosis method based on deep convolution domain anti-migration learning. Background technique [0002] Rotating mechanical equipment is widely used in metallurgy, aviation, transportation, chemical and energy industries, and its mechanical structure is developing in the direction of large-scale, heavy-duty, precision and high-speed. Condition monitoring and fault diagnosis are important means to ensure the long-term safe and reliable operation of modern industrial equipment. [0003] Since rotating machinery often works in an industrial environment with variable working conditions during the entire service period, the probability of failure is high and it is easy to be damaged. In the environment of variable working conditions (such as: different speeds, different loads), there is a problem that it is difficult to dire...

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Problems solved by technology

[0005] Aiming at the above-mentioned deficiencies in the prior art, the fault diagnosis method based on deep convolutional domain adversarial transfer learning provided by the present invention solves the problem of obtaining a large number of labeled samples of rotating machinery in the existing rotating machinery fault diagnosis method (that is, known Fault type samples) are relatively difficult, so that there are fewer labeled samples under the current working conditions, resulting in a problem of low fault diagnosis accuracy for the current samples to be tested

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