Jinhua Wang , Wenbao Cao , Jie Cao , Li Chen , Yanhong Ma
{"title":"基于多尺度自适应超图卷积神经网络的故障诊断方法","authors":"Jinhua Wang , Wenbao Cao , Jie Cao , Li Chen , Yanhong Ma","doi":"10.1016/j.apacoust.2025.110889","DOIUrl":null,"url":null,"abstract":"<div><div>In the research on fault diagnosis using deep learning, there is a lack of effective cross-scale feature association modeling, which overlooks the potential correlations between signals at different scales, resulting in weak generalization capabilities. This paper proposes a multi-scale feature construction space Supergraph integrated with physical prior knowledge, and based on this method, introduces a Multi-Scale Adaptive Supergraph Convolutional Neural Network (MS-ASGCN). The vibration signals are decomposed into different frequency bands, and features at various scales are extracted while incorporating prior knowledge. Local topological graphs are constructed using various weighted metrics, and these local graphs together form a spatial Supergraph, which serves as the input for the model. A dual-channel Graph Convolutional Network (GCN) is employed to extract features, and an attention mechanism is introduced to adaptively assign weights to different channels, achieving deep feature fusion. Experiments on two benchmark datasets demonstrate that MS-ASGCN effectively improves model accuracy and exhibits good stability and generalization capabilities.</div></div>","PeriodicalId":55506,"journal":{"name":"Applied Acoustics","volume":"240 ","pages":"Article 110889"},"PeriodicalIF":3.4000,"publicationDate":"2025-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fault diagnosis method based on multi-scale adaptive super-graph convolutional neural networks\",\"authors\":\"Jinhua Wang , Wenbao Cao , Jie Cao , Li Chen , Yanhong Ma\",\"doi\":\"10.1016/j.apacoust.2025.110889\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In the research on fault diagnosis using deep learning, there is a lack of effective cross-scale feature association modeling, which overlooks the potential correlations between signals at different scales, resulting in weak generalization capabilities. This paper proposes a multi-scale feature construction space Supergraph integrated with physical prior knowledge, and based on this method, introduces a Multi-Scale Adaptive Supergraph Convolutional Neural Network (MS-ASGCN). The vibration signals are decomposed into different frequency bands, and features at various scales are extracted while incorporating prior knowledge. Local topological graphs are constructed using various weighted metrics, and these local graphs together form a spatial Supergraph, which serves as the input for the model. A dual-channel Graph Convolutional Network (GCN) is employed to extract features, and an attention mechanism is introduced to adaptively assign weights to different channels, achieving deep feature fusion. Experiments on two benchmark datasets demonstrate that MS-ASGCN effectively improves model accuracy and exhibits good stability and generalization capabilities.</div></div>\",\"PeriodicalId\":55506,\"journal\":{\"name\":\"Applied Acoustics\",\"volume\":\"240 \",\"pages\":\"Article 110889\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2025-06-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Acoustics\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0003682X25003615\",\"RegionNum\":2,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ACOUSTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Acoustics","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0003682X25003615","RegionNum":2,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ACOUSTICS","Score":null,"Total":0}
Fault diagnosis method based on multi-scale adaptive super-graph convolutional neural networks
In the research on fault diagnosis using deep learning, there is a lack of effective cross-scale feature association modeling, which overlooks the potential correlations between signals at different scales, resulting in weak generalization capabilities. This paper proposes a multi-scale feature construction space Supergraph integrated with physical prior knowledge, and based on this method, introduces a Multi-Scale Adaptive Supergraph Convolutional Neural Network (MS-ASGCN). The vibration signals are decomposed into different frequency bands, and features at various scales are extracted while incorporating prior knowledge. Local topological graphs are constructed using various weighted metrics, and these local graphs together form a spatial Supergraph, which serves as the input for the model. A dual-channel Graph Convolutional Network (GCN) is employed to extract features, and an attention mechanism is introduced to adaptively assign weights to different channels, achieving deep feature fusion. Experiments on two benchmark datasets demonstrate that MS-ASGCN effectively improves model accuracy and exhibits good stability and generalization capabilities.
期刊介绍:
Since its launch in 1968, Applied Acoustics has been publishing high quality research papers providing state-of-the-art coverage of research findings for engineers and scientists involved in applications of acoustics in the widest sense.
Applied Acoustics looks not only at recent developments in the understanding of acoustics but also at ways of exploiting that understanding. The Journal aims to encourage the exchange of practical experience through publication and in so doing creates a fund of technological information that can be used for solving related problems. The presentation of information in graphical or tabular form is especially encouraged. If a report of a mathematical development is a necessary part of a paper it is important to ensure that it is there only as an integral part of a practical solution to a problem and is supported by data. Applied Acoustics encourages the exchange of practical experience in the following ways: • Complete Papers • Short Technical Notes • Review Articles; and thereby provides a wealth of technological information that can be used to solve related problems.
Manuscripts that address all fields of applications of acoustics ranging from medicine and NDT to the environment and buildings are welcome.