{"title":"基于包含时空卷积的自注意机制网络的多传感器机械设备剩余使用寿命预测","authors":"Xu Yang, Lin Tang, Jian Huang","doi":"10.1177/09596518241269642","DOIUrl":null,"url":null,"abstract":"Driven by the limitations of spatial feature extraction in graph learning methods of multi-sensor mechanism equipment, this paper proposes a spatio-temporal self-attention mechanism network (STCAN) that integrates spatial relationships and time series information to predict the remaining useful life (RUL). Firstly, a graph convolutional network (GCN) is applied to extract the spatial correlation characteristics and fused with the self-attention mechanism network to obtain the global and local spatial features. Subsequently, a dilated convolutional network (DCN) is integrated into the self-attention mechanism network, to extract the global and multi-step temporal features and mitigate long-term dependency issues. Finally, the extracted spatio-temporal features are used to predict the equipment’s RUL through fully connected layers. The experimental results demonstrate that STCAN outperforms some existing methods in terms of RUL prediction.","PeriodicalId":20638,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Remaining useful life prediction for multi-sensor mechanical equipment based on self-attention mechanism network incorporating spatio-temporal convolution\",\"authors\":\"Xu Yang, Lin Tang, Jian Huang\",\"doi\":\"10.1177/09596518241269642\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Driven by the limitations of spatial feature extraction in graph learning methods of multi-sensor mechanism equipment, this paper proposes a spatio-temporal self-attention mechanism network (STCAN) that integrates spatial relationships and time series information to predict the remaining useful life (RUL). Firstly, a graph convolutional network (GCN) is applied to extract the spatial correlation characteristics and fused with the self-attention mechanism network to obtain the global and local spatial features. Subsequently, a dilated convolutional network (DCN) is integrated into the self-attention mechanism network, to extract the global and multi-step temporal features and mitigate long-term dependency issues. Finally, the extracted spatio-temporal features are used to predict the equipment’s RUL through fully connected layers. The experimental results demonstrate that STCAN outperforms some existing methods in terms of RUL prediction.\",\"PeriodicalId\":20638,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-08-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/09596518241269642\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/09596518241269642","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Remaining useful life prediction for multi-sensor mechanical equipment based on self-attention mechanism network incorporating spatio-temporal convolution
Driven by the limitations of spatial feature extraction in graph learning methods of multi-sensor mechanism equipment, this paper proposes a spatio-temporal self-attention mechanism network (STCAN) that integrates spatial relationships and time series information to predict the remaining useful life (RUL). Firstly, a graph convolutional network (GCN) is applied to extract the spatial correlation characteristics and fused with the self-attention mechanism network to obtain the global and local spatial features. Subsequently, a dilated convolutional network (DCN) is integrated into the self-attention mechanism network, to extract the global and multi-step temporal features and mitigate long-term dependency issues. Finally, the extracted spatio-temporal features are used to predict the equipment’s RUL through fully connected layers. The experimental results demonstrate that STCAN outperforms some existing methods in terms of RUL prediction.
期刊介绍:
Systems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering refleSystems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering reflects this diversity by giving prominence to experimental application and industrial studies.
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This journal is a member of the Committee on Publication Ethics (COPE).cts this diversity by giving prominence to experimental application and industrial studies.