Rolling bearing fault diagnosis method based on multi-modal knowledge graph
The invention discloses a rolling bearing fault diagnosis method based on a multi-modal knowledge graph, and the method comprises the steps: carrying out the sliding window processing of a vibration signal, obtaining a signal entity node, carrying out the preprocessing, and obtaining the multi-modal...
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Main Authors | , , , , |
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Format | Patent |
Language | Chinese English |
Published |
22.12.2023
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Subjects | |
Online Access | Get full text |
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Summary: | The invention discloses a rolling bearing fault diagnosis method based on a multi-modal knowledge graph, and the method comprises the steps: carrying out the sliding window processing of a vibration signal, obtaining a signal entity node, carrying out the preprocessing, and obtaining the multi-modal data of the signal entity node; fusing the multi-modal features by adopting a low-rank multi-modal fusion method to obtain representation of signal entity nodes, extracting a relation and attribute value nodes based on a description text, establishing connection between the nodes according to a semantic rule, and preliminarily constructing a signal-based multi-modal knowledge graph; according to the method, attribute value nodes and relations are embedded and coded by using a relation cascade attention network, then a two-dimensional convolution knowledge graph is embedded as a decoder to obtain a score sequence of candidate triads, link prediction is carried out, and fault diagnosis of the rolling bearing is real |
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Bibliography: | Application Number: CN202311348240 |