Multi-target-domain equipment fault diagnosis method based on AdaDCLF

A technology for equipment failure and diagnosis methods, applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as the performance degradation of training models, and achieve the effect of enhancing generalization ability, improving diagnosis accuracy, and realizing intelligent diagnosis.

Pending Publication Date: 2022-03-11
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, these domain adaptation methods are often based on a single target domain. In practical applications, there may be more than one

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  • Multi-target-domain equipment fault diagnosis method based on AdaDCLF
  • Multi-target-domain equipment fault diagnosis method based on AdaDCLF
  • Multi-target-domain equipment fault diagnosis method based on AdaDCLF

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specific Embodiment

[0080] The original data set of the experiment has collected data under four different working conditions, with a total of 6000 samples. According to different working conditions, the data is divided into source domain and three target domain data. The source domain data has 3000 samples, and each target domain data has 1000 samples. The source domain data is labeled, and the target domain data is unlabeled. The source domain data is labeled 0, and the three target domain data are labeled 1 to 3. The source domain data is randomly divided into the original training set and the test set at a ratio of 4:1. The source domain data is used for training, and the AdaDCLF method is used to generalize the model on multiple target domains through the source domain and target domain data. Finally, the target domain data is input into the final model to obtain the final fault diagnosis result.

[0081] The experimental environment of the present invention is: CPU is CoreTMi7-6700K@4.00G...

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Abstract

The invention discloses an AdaDCLF-based multi-target domain equipment fault diagnosis method, which combines an MK-MMD distance to measure the distribution deviation of a source domain and a target domain, then adaptively adjusts a domain confusion loss factor, and focuses on the importance of target domain data corresponding to the maximum domain confusion loss. According to the AdaDCLF adaptive adjustment domain confusion loss factor method, the generalization ability of the model on the multi-target domain is enhanced, the diagnosis accuracy of the equipment fault diagnosis model on the multi-target domain is remarkably improved, and the problem that the generalization ability of a single-target domain adaptive algorithm on the multi-target domain is not strong is solved, so that intelligent diagnosis of equipment faults is realized.

Description

technical field [0001] The invention relates to a device fault diagnosis method, in particular to an AdaDCLF-based multi-target domain device fault diagnosis method. Background technique [0002] With the rapid development of industrial big data and the Internet of Things in the context of Industry 4.0, intelligent health management of key industrial equipment in factories has become more and more important, which is also an urgent problem that smart factories need to solve. In actual industrial applications, most equipment fault location and maintenance rely on past experience or expert knowledge. This diagnosis method requires a lot of manpower and time costs, and the efficiency is not high. In response to this problem, intelligent equipment fault diagnosis combines artificial intelligence technology and fault diagnosis technology to become an important branch of intelligent health management. Traditional traditional machine learning methods, including support vector mach...

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06N3/047G06N3/045G06F18/2415
Inventor 何志伟刘才明郑骁蓉董哲康高明煜
Owner HANGZHOU DIANZI UNIV
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