{"title":"使用基于可转移深度强化学习的方法预测复合材料结构的疲劳寿命","authors":"Cheng Liu, Yan Chen, Xuebing Xu","doi":"10.1016/j.compstruct.2024.118727","DOIUrl":null,"url":null,"abstract":"<div><div>Accurately predicting the remaining useful life (RUL) of Carbon Fiber Reinforced Polymer (CFRP) structures under fatigue loading is crucial for enhancing safety and minimizing maintenance costs, especially in industries like aerospace and automotive. However, the complex physical properties of CFRP, combined with the scarcity of real-world damage-condition data, make this task extremely challenging. To address these issues, we propose a novel deep reinforcement learning (DRL)-based prognostic method. Our approach integrates Denoising Autoencoder (DAE) and Transformer architectures to construct a powerful DRL Policy Network, capable of extracting high-quality features from X-ray records to capture the subtle progression of damage in CFRP structures. Additionally, we employ advanced data augmentation techniques to overcome the limitations of small datasets and introduce transfer learning to extend the model’s generalization capabilities across different CFRP structures. By pre-training on diverse CFRP datasets, our model achieves highly accurate RUL predictions for new designs, even with minimal labeled data from the target structure. Experimental results demonstrate that our method significantly outperforms current state-of-the-art (SOTA) techniques, offering a scalable, efficient, and practical solution for the real-world monitoring and prognostics of CFRP structures, with broad potential for industrial applications.</div></div>","PeriodicalId":281,"journal":{"name":"Composite Structures","volume":"353 ","pages":"Article 118727"},"PeriodicalIF":6.3000,"publicationDate":"2024-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Fatigue life prognosis of composite structures using a transferable deep reinforcement learning-based approach\",\"authors\":\"Cheng Liu, Yan Chen, Xuebing Xu\",\"doi\":\"10.1016/j.compstruct.2024.118727\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurately predicting the remaining useful life (RUL) of Carbon Fiber Reinforced Polymer (CFRP) structures under fatigue loading is crucial for enhancing safety and minimizing maintenance costs, especially in industries like aerospace and automotive. However, the complex physical properties of CFRP, combined with the scarcity of real-world damage-condition data, make this task extremely challenging. To address these issues, we propose a novel deep reinforcement learning (DRL)-based prognostic method. Our approach integrates Denoising Autoencoder (DAE) and Transformer architectures to construct a powerful DRL Policy Network, capable of extracting high-quality features from X-ray records to capture the subtle progression of damage in CFRP structures. Additionally, we employ advanced data augmentation techniques to overcome the limitations of small datasets and introduce transfer learning to extend the model’s generalization capabilities across different CFRP structures. By pre-training on diverse CFRP datasets, our model achieves highly accurate RUL predictions for new designs, even with minimal labeled data from the target structure. Experimental results demonstrate that our method significantly outperforms current state-of-the-art (SOTA) techniques, offering a scalable, efficient, and practical solution for the real-world monitoring and prognostics of CFRP structures, with broad potential for industrial applications.</div></div>\",\"PeriodicalId\":281,\"journal\":{\"name\":\"Composite Structures\",\"volume\":\"353 \",\"pages\":\"Article 118727\"},\"PeriodicalIF\":6.3000,\"publicationDate\":\"2024-11-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Composite Structures\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0263822324008559\",\"RegionNum\":2,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MATERIALS SCIENCE, COMPOSITES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Composite Structures","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0263822324008559","RegionNum":2,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MATERIALS SCIENCE, COMPOSITES","Score":null,"Total":0}
Fatigue life prognosis of composite structures using a transferable deep reinforcement learning-based approach
Accurately predicting the remaining useful life (RUL) of Carbon Fiber Reinforced Polymer (CFRP) structures under fatigue loading is crucial for enhancing safety and minimizing maintenance costs, especially in industries like aerospace and automotive. However, the complex physical properties of CFRP, combined with the scarcity of real-world damage-condition data, make this task extremely challenging. To address these issues, we propose a novel deep reinforcement learning (DRL)-based prognostic method. Our approach integrates Denoising Autoencoder (DAE) and Transformer architectures to construct a powerful DRL Policy Network, capable of extracting high-quality features from X-ray records to capture the subtle progression of damage in CFRP structures. Additionally, we employ advanced data augmentation techniques to overcome the limitations of small datasets and introduce transfer learning to extend the model’s generalization capabilities across different CFRP structures. By pre-training on diverse CFRP datasets, our model achieves highly accurate RUL predictions for new designs, even with minimal labeled data from the target structure. Experimental results demonstrate that our method significantly outperforms current state-of-the-art (SOTA) techniques, offering a scalable, efficient, and practical solution for the real-world monitoring and prognostics of CFRP structures, with broad potential for industrial applications.
期刊介绍:
The past few decades have seen outstanding advances in the use of composite materials in structural applications. There can be little doubt that, within engineering circles, composites have revolutionised traditional design concepts and made possible an unparalleled range of new and exciting possibilities as viable materials for construction. Composite Structures, an International Journal, disseminates knowledge between users, manufacturers, designers and researchers involved in structures or structural components manufactured using composite materials.
The journal publishes papers which contribute to knowledge in the use of composite materials in engineering structures. Papers deal with design, research and development studies, experimental investigations, theoretical analysis and fabrication techniques relevant to the application of composites in load-bearing components for assemblies, ranging from individual components such as plates and shells to complete composite structures.