Intelligent fault diagnosis method based on deep adversarial domain self-adaption

Abstract

The invention provides an intelligent fault diagnosis method based on deep adversarial domain self-adaption. The method comprises the steps: collecting vibration signals of a rotating machine under different working conditions through a sensor, and carrying out the signal segmentation of a data set under different working conditions through a moving time window; discriminative features in the dataset are extracted; constructing a deep adversarial domain adaptive network by combining a feature extractor and a domain discriminator, and extracting domain invariant features under two working conditions; adopting a training strategy of the adversarial network to jointly train the two-stream network model until the model converges, and using the trained category classifier to identify the bearing health state of the target domain data set lacking the fault label. According to the method, fault diagnosis is carried out on the working condition with insufficient data information by means of the working condition with rich data information, migration of diagnosis knowledge is completed. Meanwhile, a deep learning network is constructed, dependence on expert knowledge in a traditional diagnosis method is overcome, and an effective tool is provided for reducing the cost of a future intelligent fault diagnosis system.

Description

technical field [0001] The invention relates to a rolling bearing state evaluation method, in particular to an intelligent fault diagnosis method based on depth confrontation domain self-adaptation. Background technique [0002] Rolling bearings are the key components of modern machinery and equipment, and are widely used in aerospace, construction machinery, ship equipment, water conservancy projects and other fields. The health status and performance of rolling bearings directly affect the safety and reliability of mechanical equipment. Bearing failure may lead to the shutdown of the entire mechanical system, resulting in unimaginable economic losses. Therefore, the condition monitoring of rolling bearings plays a vital role in ensuring the safe operation of equipment and reducing unexpected shutdown losses. [0003] The status of the monitored equipment can be judged by analyzing the vibration signal collected by the sensor. At present, the most popular data-driven int...

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Problems solved by technology

[0004] Aiming at the deficiencies in the prior art, the purpose of the present invention is to provide an intelligent fault diagnosis method based on deep confrontational domain self-adaptation, which solves the deficiencies of the traditional intelligent diagnosis method in industrial applications, and overcomes the problem of when the training set and test In order to solve the problems of performance deterioration of the intelligent diagnosis method when the distribution is different due to the change of the working conditions, a new fault diagnosis method based on transfer learning is explored, which provides an effective solution for improving the generalization performance of the intelligent fault diagnosis system.

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