Mechanical fault diagnosis method based on migration relation network

A relational network and mechanical fault technology, applied in the field of machine learning, can solve problems such as difficulty in adapting and meeting the application requirements of intelligent diagnosis of mechanical faults, low accuracy of deep migration diagnosis, and ignoring the degree of correlation between source machinery and target machinery of diagnosis knowledge.
CN111709448AActive Publication Date: 2020-09-25XI AN JIAOTONG UNIV
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Patent Information

Authority / Receiving Office
CN Β· China
Current Assignee / Owner
XI AN JIAOTONG UNIV
Publication Date
2020-09-25

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Abstract

The invention discloses a mechanical fault diagnosis method based on a migration relationship network. The method comprises the following steps: constructing source domain and target domain data of the migration relationship network; constructing a training set and a test set of migration relationship network samples; constructing a migration relation network capable of detecting the mechanical fault type; and training the migration relation network to obtain a mechanical fault diagnosis model, and performing test and performance evaluation on the final model. The invention provides a migration relationship network with a Siamese structure, which combines a relationship network in meta-learning and migration learning for the first time. A double-channel relation network is constructed by utilizing a Siamese structure, all data of a source domain and label-free data of a target domain are respectively input, information of the target domain is fully considered during additional training, and the accuracy of fault diagnosis is greatly improved. MK-MMD is fused into a network, so that the probability distribution distance between two different fields is effectively reduced, and the application of laboratory data to actual mechanical fault diagnosis becomes possible.
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Description

technical field

[0001] The invention belongs to the technical field of machine learning, and in particular relates to a mechanical fault diagnosis method based on a migration relational network. Background technique

[0002] With the rapid rise and vigorous development of the industrial Internet and the Internet of Things technology, the multi-source sensor network of mechanical equipment is densely arranged, and the amount of monitoring data interaction is increasing day by day, making mechanical fault diagnosis enter the "big data" era, which provides a comprehensive basis for the comprehensive control of equipment. Healthy service status provides big information and big knowledge, and how to effectively tap the potential value behind the big data of machinery has become a frontier hotspot and research difficulty in ensuring the safe operation of equipment driven by big data.

[0003] Intelligent fault diagnosis is an important means to ensure the safe operation of mechani...

Claims

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